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https://api.github.com/repos/huggingface/datasets/issues/1952 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1952/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1952/comments | https://api.github.com/repos/huggingface/datasets/issues/1952/events | https://github.com/huggingface/datasets/pull/1952 | 817,428,160 | MDExOlB1bGxSZXF1ZXN0NTgwOTIyNjQw | 1,952 | Handle timeouts | {
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"I never said the calls were hanging indefinitely, what we need is quite different - in the firewalled env with a network, there should be no network calls or they should fail instantly.\r\n\r\nTo make this work I suppose on top of this PR we need:\r\n1. `DATASETS_OFFLINE` env var to force set timeout to 0 globally... | 1,614,351,727,000 | 1,614,608,964,000 | 1,614,608,964,000 | MEMBER | null | false | {
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} | As noticed in https://github.com/huggingface/datasets/issues/1939, timeouts were not properly handled when loading a dataset.
This caused the connection to hang indefinitely when working in a firewalled environment cc @stas00
I added a default timeout, and included an option to our offline environment for tests to be able to simulate both connection errors and timeout errors (previously it was simulating connection errors only).
Now networks calls don't hang indefinitely.
The default timeout is set to 10sec (we might reduce it). | {
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"@mariosasko This is kinda cool! "
] | 1,614,351,385,000 | 1,615,429,106,000 | 1,614,353,426,000 | CONTRIBUTOR | null | false | {
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} | One thing I've noticed while going through the codebase is the usage of `scripts` in `setup.py`. This [answer](https://stackoverflow.com/a/28119736/14095927) on SO explains it nicely why it's better to use `entry_points` instead of `scripts`. To add cross-platform support to the CLI, this PR replaces `scripts` with `entry_points` in `setup.py` and moves datasets-cli to src/datasets/commands/datasets_cli.py. All *.md and *.rst files are updated accordingly. The same changes were made in the transformers repo to add cross-platform ([link to PR](https://github.com/huggingface/transformers/pull/4131)). | {
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} | 1) updated fields which were missing earlier
2) added tags to README
3) updated a few fields of README
4) new dataset_infos.json and dummy files | {
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"Hi @gchhablani :)\r\nThanks for proposing your help !\r\n\r\nI'll be doing a refactor of some parts related to filtering in the scope of https://github.com/huggingface/datasets/issues/1877\r\nSo I would first wait for this refactor to be done before working on the filtering. In particular because I plan to make th... | 1,614,308,017,000 | 1,614,367,109,000 | null | CONTRIBUTOR | null | null | null | Hi @lhoestq,
As mentioned in Issue #1796, I would love to work on enabling fast filtering/mapping. Can you please share the expectations? It would be great if you could point me to the relevant methods/files involved. Or the docs or maybe an overview of `arrow_dataset.py`. I only ask this because I am having trouble getting started ;-;
Any help would be appreciated.
Thanks,
Gunjan | {
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"These warnings are showed when there's a call to `.map` to say to the user that a dataset is reloaded from the cache instead of being recomputed.\r\nThey are warnings since we want to make sure the users know that it's not recomputed.",
"Thank you for explaining the intention, @lhoestq \r\n\r\n1. Could it be the... | 1,614,278,017,000 | 1,614,302,824,000 | null | CONTRIBUTOR | null | null | null | on master I get this with `--dataset_name wmt16 --dataset_config ro-en`:
```
WARNING:datasets.arrow_dataset:Loading cached processed dataset at /home/stas/.cache/huggingface/datasets/wmt16/ro-en/1.0.0/9dc00622c30446e99c4c63d12a484ea4fb653f2f37c867d6edcec839d7eae50f/cache-2e01bead8cf42e26.arrow
WARNING:datasets.arrow_dataset:Loading cached processed dataset at /home/stas/.cache/huggingface/datasets/wmt16/ro-en/1.0.0/9dc00622c30446e99c4c63d12a484ea4fb653f2f37c867d6edcec839d7eae50f/cache-ac3bebaf4f91f776.arrow
WARNING:datasets.arrow_dataset:Loading cached processed dataset at /home/stas/.cache/huggingface/datasets/wmt16/ro-en/1.0.0/9dc00622c30446e99c4c63d12a484ea4fb653f2f37c867d6edcec839d7eae50f/cache-810c3e61259d73a9.arrow
```
why are those WARNINGs? Should be INFO, no?
warnings should only be used when a user needs to pay attention to something, this is just informative - I'd even say it should be DEBUG, but definitely not WARNING.
Thank you.
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} | In #1883 were added the not in-place transforms `flatten`, `remove_columns`, `rename_column` and `cast`.
I added them to the documentation and added a paragraph on how to use them
You can preview the documentation [here](https://28862-250213286-gh.circle-artifacts.com/0/docs/_build/html/processing.html#renaming-removing-casting-and-flattening-columns)
I also added these methods to the DatasetDict class. | {
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"@lhoestq question about public API: `keep_in_memory` or just `in_memory`?",
"For consistence I'd say `keep_in_memory`, but no strong opinion.",
"@lhoestq done!"
] | 1,614,265,813,000 | 1,615,542,168,000 | 1,615,542,168,000 | MEMBER | null | false | {
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Analogue to #1943.
If finally, the scripts should be used instead, at least we can reuse the tests here. | {
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"sorry my mistake, datasets were overwritten closing now, thanks a lot"
] | 1,614,258,585,000 | 1,614,259,235,000 | 1,614,259,226,000 | NONE | null | null | null | Hi
I am trying to concatenate a list of huggingface datastes as:
` train_dataset = datasets.concatenate_datasets(train_datasets)
`
Here is the `train_datasets` when I print:
```
[Dataset({
features: ['attention_mask', 'idx', 'input_ids', 'label', 'question1', 'question2', 'token_type_ids'],
num_rows: 120361
}), Dataset({
features: ['attention_mask', 'idx', 'input_ids', 'label', 'question1', 'question2', 'token_type_ids'],
num_rows: 2670
}), Dataset({
features: ['attention_mask', 'idx', 'input_ids', 'label', 'question1', 'question2', 'token_type_ids'],
num_rows: 6944
}), Dataset({
features: ['attention_mask', 'idx', 'input_ids', 'label', 'question1', 'question2', 'token_type_ids'],
num_rows: 38140
}), Dataset({
features: ['attention_mask', 'idx', 'input_ids', 'label', 'question1', 'question2', 'token_type_ids'],
num_rows: 173711
}), Dataset({
features: ['attention_mask', 'idx', 'input_ids', 'label', 'question1', 'question2', 'token_type_ids'],
num_rows: 1655
}), Dataset({
features: ['attention_mask', 'idx', 'input_ids', 'label', 'question1', 'question2', 'token_type_ids'],
num_rows: 4274
}), Dataset({
features: ['attention_mask', 'idx', 'input_ids', 'label', 'question1', 'question2', 'token_type_ids'],
num_rows: 2019
}), Dataset({
features: ['attention_mask', 'idx', 'input_ids', 'label', 'question1', 'question2', 'token_type_ids'],
num_rows: 2109
}), Dataset({
features: ['attention_mask', 'idx', 'input_ids', 'label', 'question1', 'question2', 'token_type_ids'],
num_rows: 11963
})]
```
I am getting the following error:
`AttributeError: 'DatasetDict' object has no attribute 'concatenate_datasets'
`
I was wondering if you could help me with this issue, thanks a lot | {
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https://api.github.com/repos/huggingface/datasets/issues/1944 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1944/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1944/comments | https://api.github.com/repos/huggingface/datasets/issues/1944/events | https://github.com/huggingface/datasets/pull/1944 | 816,267,216 | MDExOlB1bGxSZXF1ZXN0NTc5OTU2Nzc3 | 1,944 | Add Turkish News Category Dataset (270K - Lite Version) | {
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"I updated your suggestions. Thank you very much for your support. @lhoestq ",
"> Thanks for changing to ClassLabel :)\r\n> This is all good now !\r\n> \r\n> However I can see changes in other files than the ones for interpress_news_category_tr_lite, can you please fix that ?\r\n> To do so you can create another ... | 1,614,246,322,000 | 1,614,707,201,000 | 1,614,623,001,000 | CONTRIBUTOR | null | false | {
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} | This PR adds the Turkish News Categories Dataset (270K - Lite Version) dataset which is a text classification dataset by me, @basakbuluz and @serdarakyol.
This dataset contains the same news from the current [interpress_news_category_tr dataset](https://huggingface.co/datasets/interpress_news_category_tr) but contains less information, OCR errors are reduced, can be easily separated, and can be divided into 10 classes ("kültürsanat", "ekonomi", "siyaset", "eğitim", "dünya", "spor", "teknoloji", "magazin", "sağlık", "gündem") were rearranged.
@SBrandeis @lhoestq, can you please review this PR?
| {
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https://api.github.com/repos/huggingface/datasets/issues/1943 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1943/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1943/comments | https://api.github.com/repos/huggingface/datasets/issues/1943/events | https://github.com/huggingface/datasets/pull/1943 | 816,160,453 | MDExOlB1bGxSZXF1ZXN0NTc5ODY5NTk0 | 1,943 | Implement Dataset from JSON and JSON Lines | {
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"Thanks @lhoestq. I was trying to follow @thomwolf suggestion about integrating that script but as `from_json` method...\r\n> Note that I don't think this is necessary a breaking change, we can still keep the old scripts around\r\n\r\nDo you think there is a better way of doing it?\r\n\r\nI was trying to implement ... | 1,614,237,453,000 | 1,616,060,528,000 | 1,616,060,528,000 | MEMBER | null | false | {
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} | Implement `Dataset.from_jsonl`. | {
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https://api.github.com/repos/huggingface/datasets/issues/1942 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1942/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1942/comments | https://api.github.com/repos/huggingface/datasets/issues/1942/events | https://github.com/huggingface/datasets/issues/1942 | 816,037,520 | MDU6SXNzdWU4MTYwMzc1MjA= | 1,942 | [experiment] missing default_experiment-1-0.arrow | {
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"Hi !\r\n\r\nThe cache at `~/.cache/huggingface/metrics` stores the users data for metrics computations (hence the arrow files).\r\n\r\nHowever python modules (i.e. dataset scripts, metric scripts) are stored in `~/.cache/huggingface/modules/datasets_modules`.\r\n\r\nIn particular the metrics are cached in `~/.cach... | 1,614,222,135,000 | 1,614,623,611,000 | null | CONTRIBUTOR | null | null | null | the original report was pretty bad and incomplete - my apologies!
Please see the complete version here: https://github.com/huggingface/datasets/issues/1942#issuecomment-786336481
------------
As mentioned here https://github.com/huggingface/datasets/issues/1939 metrics don't get cached, looking at my local `~/.cache/huggingface/metrics` - there are many `*.arrow.lock` files but zero metrics files.
w/o the network I get:
```
FileNotFoundError: [Errno 2] No such file or directory: '~/.cache/huggingface/metrics/sacrebleu/default/default_experiment-1-0.arrow
```
there is just `~/.cache/huggingface/metrics/sacrebleu/default/default_experiment-1-0.arrow.lock`
I did run the same `run_seq2seq.py` script on the instance with network and it worked just fine, but only the lock file was left behind.
this is with master.
Thank you. | {
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https://api.github.com/repos/huggingface/datasets/issues/1941 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1941/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1941/comments | https://api.github.com/repos/huggingface/datasets/issues/1941/events | https://github.com/huggingface/datasets/issues/1941 | 815,985,167 | MDU6SXNzdWU4MTU5ODUxNjc= | 1,941 | Loading of FAISS index fails for index_name = 'exact' | {
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"Thanks for reporting ! I'm taking a look",
"Index training was missing, I fixed it here: https://github.com/huggingface/datasets/commit/f5986c46323583989f6ed1dabaf267854424a521\r\n\r\nCan you try again please ?",
"Works great 👍 I just put a minor comment on the commit, I think you meant to pass the `train_siz... | 1,614,216,654,000 | 1,614,263,326,000 | 1,614,263,326,000 | CONTRIBUTOR | null | null | null | Hi,
It looks like loading of FAISS index now fails when using index_name = 'exact'.
For example, from the RAG [model card](https://huggingface.co/facebook/rag-token-nq?fbclid=IwAR3bTfhls5U_t9DqsX2Vzb7NhtRHxJxfQ-uwFT7VuCPMZUM2AdAlKF_qkI8#usage).
Running `transformers==4.3.2` and datasets installed from source on latest `master` branch.
```bash
(venv) sergey_mkrtchyan datasets (master) $ python
Python 3.8.6 (v3.8.6:db455296be, Sep 23 2020, 13:31:39)
[Clang 6.0 (clang-600.0.57)] on darwin
Type "help", "copyright", "credits" or "license" for more information.
>>> from transformers import RagTokenizer, RagRetriever, RagTokenForGeneration
>>> tokenizer = RagTokenizer.from_pretrained("facebook/rag-token-nq")
>>> retriever = RagRetriever.from_pretrained("facebook/rag-token-nq", index_name="exact", use_dummy_dataset=True)
Using custom data configuration dummy.psgs_w100.nq.no_index-dummy=True,with_index=False
Reusing dataset wiki_dpr (/Users/sergey_mkrtchyan/.cache/huggingface/datasets/wiki_dpr/dummy.psgs_w100.nq.no_index-dummy=True,with_index=False/0.0.0/8a97e0f4fa5bc46e179474db6a61b09d5d2419d2911835bd3f91d110c936d8bb)
Using custom data configuration dummy.psgs_w100.nq.exact-50b6cda57ff32ab4
Reusing dataset wiki_dpr (/Users/sergey_mkrtchyan/.cache/huggingface/datasets/wiki_dpr/dummy.psgs_w100.nq.exact-50b6cda57ff32ab4/0.0.0/8a97e0f4fa5bc46e179474db6a61b09d5d2419d2911835bd3f91d110c936d8bb)
0%| | 0/10 [00:00<?, ?it/s]
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/sergey_mkrtchyan/workspace/cformers/venv/lib/python3.8/site-packages/transformers/models/rag/retrieval_rag.py", line 425, in from_pretrained
return cls(
File "/Users/sergey_mkrtchyan/workspace/cformers/venv/lib/python3.8/site-packages/transformers/models/rag/retrieval_rag.py", line 387, in __init__
self.init_retrieval()
File "/Users/sergey_mkrtchyan/workspace/cformers/venv/lib/python3.8/site-packages/transformers/models/rag/retrieval_rag.py", line 458, in init_retrieval
self.index.init_index()
File "/Users/sergey_mkrtchyan/workspace/cformers/venv/lib/python3.8/site-packages/transformers/models/rag/retrieval_rag.py", line 284, in init_index
self.dataset = load_dataset(
File "/Users/sergey_mkrtchyan/workspace/huggingface/datasets/src/datasets/load.py", line 750, in load_dataset
ds = builder_instance.as_dataset(split=split, ignore_verifications=ignore_verifications, in_memory=keep_in_memory)
File "/Users/sergey_mkrtchyan/workspace/huggingface/datasets/src/datasets/builder.py", line 734, in as_dataset
datasets = utils.map_nested(
File "/Users/sergey_mkrtchyan/workspace/huggingface/datasets/src/datasets/utils/py_utils.py", line 195, in map_nested
return function(data_struct)
File "/Users/sergey_mkrtchyan/workspace/huggingface/datasets/src/datasets/builder.py", line 769, in _build_single_dataset
post_processed = self._post_process(ds, resources_paths)
File "/Users/sergey_mkrtchyan/.cache/huggingface/modules/datasets_modules/datasets/wiki_dpr/8a97e0f4fa5bc46e179474db6a61b09d5d2419d2911835bd3f91d110c936d8bb/wiki_dpr.py", line 205, in _post_process
dataset.add_faiss_index("embeddings", custom_index=index)
File "/Users/sergey_mkrtchyan/workspace/huggingface/datasets/src/datasets/arrow_dataset.py", line 2516, in add_faiss_index
super().add_faiss_index(
File "/Users/sergey_mkrtchyan/workspace/huggingface/datasets/src/datasets/search.py", line 416, in add_faiss_index
faiss_index.add_vectors(self, column=column, train_size=train_size, faiss_verbose=faiss_verbose)
File "/Users/sergey_mkrtchyan/workspace/huggingface/datasets/src/datasets/search.py", line 281, in add_vectors
self.faiss_index.add(vecs)
File "/Users/sergey_mkrtchyan/workspace/cformers/venv/lib/python3.8/site-packages/faiss/__init__.py", line 104, in replacement_add
self.add_c(n, swig_ptr(x))
File "/Users/sergey_mkrtchyan/workspace/cformers/venv/lib/python3.8/site-packages/faiss/swigfaiss.py", line 3263, in add
return _swigfaiss.IndexHNSW_add(self, n, x)
RuntimeError: Error in virtual void faiss::IndexHNSW::add(faiss::Index::idx_t, const float *) at /Users/runner/work/faiss-wheels/faiss-wheels/faiss/faiss/IndexHNSW.cpp:356: Error: 'is_trained' failed
>>>
```
The issue seems to be related to the scalar quantization in faiss added in this commit: 8c5220307c33f00e01c3bf7b8. Reverting it fixes the issue.
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https://api.github.com/repos/huggingface/datasets/issues/1940 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1940/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1940/comments | https://api.github.com/repos/huggingface/datasets/issues/1940/events | https://github.com/huggingface/datasets/issues/1940 | 815,770,012 | MDU6SXNzdWU4MTU3NzAwMTI= | 1,940 | Side effect when filtering data due to `does_function_return_dict` call in `Dataset.map()` | {
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"Thanks for the report !\r\n\r\nCurrently we don't have a way to let the user easily disable this behavior.\r\nHowever I agree that we should support stateful processing functions, ideally by removing `does_function_return_dict`.\r\n\r\nWe needed this function in order to know whether the `map` functions needs to w... | 1,614,194,336,000 | 1,616,513,209,000 | 1,616,513,209,000 | CONTRIBUTOR | null | null | null | Hi there!
In my codebase I have a function to filter rows in a dataset, selecting only a certain number of examples per class. The function passes a extra argument to maintain a counter of the number of dataset rows/examples already selected per each class, which are the ones I want to keep in the end:
```python
def fill_train_examples_per_class(example, per_class_limit: int, counter: collections.Counter):
label = int(example['label'])
current_counter = counter.get(label, 0)
if current_counter < per_class_limit:
counter[label] = current_counter + 1
return True
return False
```
At some point I invoke it through the `Dataset.filter()` method in the `arrow_dataset.py` module like this:
```python
...
kwargs = {"per_class_limit": train_examples_per_class_limit, "counter": Counter()}
datasets['train'] = datasets['train'].filter(fill_train_examples_per_class, num_proc=1, fn_kwargs=kwargs)
...
```
The problem is that, passing a stateful container (the counter,) provokes a side effect in the new filtered dataset obtained. This is due to the fact that at some point in `filter()`, the `map()`'s function `does_function_return_dict` is invoked in line [1290](https://github.com/huggingface/datasets/blob/96578adface7e4bc1f3e8bafbac920d72ca1ca60/src/datasets/arrow_dataset.py#L1290).
When this occurs, the state of the counter is initially modified by the effects of the function call on the 1 or 2 rows selected in lines 1288 and 1289 of the same file (which are marked as `test_inputs` & `test_indices` respectively in lines 1288 and 1289. This happens out of the control of the user (which for example can't reset the state of the counter before continuing the execution,) provoking in the end an undesired side effect in the results obtained.
In my case, the resulting dataset -despite of the counter results are ok- lacks an instance of the classes 0 and 1 (which happen to be the classes of the first two examples of my dataset.) The rest of the classes I have in my dataset, contain the right number of examples as they were not affected by the effects of `does_function_return_dict` call.
I've debugged my code extensively and made a workaround myself hardcoding the necessary stuff (basically putting `update_data=True` in line 1290,) and then I obtain the results I expected without the side effect.
Is there a way to avoid that call to `does_function_return_dict` in map()'s line 1290 ? (e.g. extracting the required information that `does_function_return_dict` returns without making the testing calls to the user function on dataset rows 0 & 1)
Thanks in advance,
Francisco Perez-Sorrosal
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"Thanks for reporting and for all the details and suggestions.\r\n\r\nI'm totally in favor of having a HF_DATASETS_OFFLINE env variable to disable manually all the connection checks, remove retries etc.\r\n\r\nMoreover you may know that the use case that you are mentioning is already supported from `datasets` 1.3.0... | 1,614,186,822,000 | 1,614,920,994,000 | 1,614,920,994,000 | CONTRIBUTOR | null | null | null | This issue comes from a need to be able to run `datasets` in a firewalled env, which currently makes the software hang until it times out, as it's unable to complete the network calls.
I propose the following approach to solving this problem, using the example of `run_seq2seq.py` as a sample program. There are 2 possible ways to going about it.
## 1. Manual
manually prepare data and metrics files, that is transfer to the firewalled instance the dataset and the metrics and run:
```
DATASETS_OFFLINE=1 run_seq2seq.py --train_file xyz.csv --validation_file xyz.csv ...
```
`datasets` must not make any network calls and if there is a logic to do that and something is missing it should assert that this or that action requires network and therefore it can't proceed.
## 2. Automatic
In some clouds one can prepare a datastorage ahead of time with a normal networked environment but which doesn't have gpus and then one switches to the gpu instance which is firewalled, but it can access all the cached data. This is the ideal situation, since in this scenario we don't have to do anything manually, but simply run the same application twice:
1. on the non-firewalled instance:
```
run_seq2seq.py --dataset_name wmt16 --dataset_config ro-en ...
```
which should download and cached everything.
2. and then immediately after on the firewalled instance, which shares the same filesystem
```
DATASETS_OFFLINE=1 run_seq2seq.py --dataset_name wmt16 --dataset_config ro-en ...
```
and the metrics and datasets should be cached by the invocation number 1 and any network calls be skipped and if the logic is missing data it should assert and not try to fetch any data from online.
## Common Issues
1. for example currently `datasets` tries to look up online datasets if the files contain json or csv, despite the paths already provided
```
if dataset and path in _PACKAGED_DATASETS_MODULES:
```
2. it has an issue with metrics. e.g. I had to manually copy `rouge/rouge.py` from the `datasets` repo to the current dir - or it was hanging.
I had to comment out `head_hf_s3(...)` calls to make things work. So all those `try: head_hf_s3(...)` shouldn't be tried with `DATASETS_OFFLINE=1`
Here is the corresponding issue for `transformers`: https://github.com/huggingface/transformers/issues/10379
Thanks. | {
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} | It was possible to create a ClassLabel without specifying the names or the number of classes.
This was causing silent issues as in #1936 and breaking the conversion methods str2int and int2str.
cc @justin-yan | {
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"Facing the same issue for [Squad](https://huggingface.co/datasets/viewer/?dataset=squad) and [TriviaQA](https://huggingface.co/datasets/viewer/?dataset=trivia_qa) datasets as well.",
"We just fixed the issue, thanks for reporting !"
] | 1,614,149,253,000 | 1,614,337,806,000 | 1,614,337,806,000 | CONTRIBUTOR | null | null | null | The page of the CommonGen data https://huggingface.co/datasets/viewer/?dataset=common_gen shows
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"Thanks ! could you maybe add a few tests in test_arrow_dataset.py to make sure from_pandas works as expected with categorical types ?\r\n\r\nIn particular I'm pretty sure that if you now try to `cast` the dataset to the same features at its current features, it will break instead of just being a no-op.\r\nThis is ... | 1,614,105,174,000 | 1,615,273,745,000 | null | CONTRIBUTOR | null | false | {
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} | @lhoestq - continuing our conversation from https://github.com/huggingface/datasets/issues/1906#issuecomment-784247014
The goal of this PR is to support `Dataset.from_pandas(df)` where the dataframe contains a Category.
Just the 4 line change below actually does seem to work:
```
>>> from datasets import Dataset
>>> import pandas as pd
>>> df = pd.DataFrame(pd.Series(["a", "b", "c", "a"], dtype="category"))
>>> ds = Dataset.from_pandas(df)
>>> ds.to_pandas()
0
0 a
1 b
2 c
3 a
>>> ds.to_pandas().dtypes
0 category
dtype: object
```
save_to_disk, etc. all seem to work as well. The main things that are theoretically "incorrect" if we leave this are:
```
>>> ds.features.type
StructType(struct<0: int64>)
```
there are a decent number of references to this property in the library, but I can't find anything that seems to actually break as a result of this being int64 vs. dictionary? I think the gist of my question is: a) do we *need* to change the dtype of Classlabel and have get_nested_type return a pyarrow.DictionaryType instead of int64? and b) do you *want* it to change? The biggest challenge I see to implementing this correctly is that the data will need to be passed in along with the pyarrow schema when instantiating the Classlabel (I *think* this is unavoidable, since the type itself doesn't contain the actual label values) which could be a fairly intrusive change - e.g. `from_arrow_schema`'s interface would need to change to include optional arrow data? Once we start going down this path of modifying the public interfaces I am admittedly feeling a little bit outside of my comfort zone
Additionally I think `int2str`, `str2int`, and `encode_example` probably won't work - but I can't find any usages of them in the library itself. | {
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"@patrickvonplaten \r\nI removed the mp3 files, dummy_data is much smaller now!"
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} | This PR adds the CoVoST2 dataset for speech translation and ASR.
https://github.com/facebookresearch/covost#covost-2
The dataset requires manual download as the download page requests an email address and the URLs are temporary.
The dummy data is a bit bigger because of the mp3 files and 36 configs. | {
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"Dataset added in release [1.5.0](https://github.com/huggingface/datasets/releases/tag/1.5.0), I think I can close this."
] | 1,614,084,796,000 | 1,616,089,904,000 | 1,616,089,904,000 | CONTRIBUTOR | null | null | null | I am going to add SST:
- **Name:** The Stanford Sentiment Treebank
- **Description:** The first corpus with fully labeled parse trees that allows for a complete analysis of the compositional effects of sentiment in language
- **Paper:** [Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank](https://nlp.stanford.edu/~socherr/EMNLP2013_RNTN.pdf)
- **Data:** https://nlp.stanford.edu/sentiment/index.html
- **Motivation:** Already requested in #353, SST is a popular dataset for Sentiment Classification
What's the difference with the [_SST-2_](https://huggingface.co/datasets/viewer/?dataset=glue&config=sst2) dataset included in GLUE? Essentially, SST-2 is a version of SST where:
- the labels were mapped from real numbers in [0.0, 1.0] to a binary label: {0, 1}
- the labels of the *sub-sentences* were included only in the training set
- the labels in the test set are obfuscated
So there is a lot more information in the original SST. The tricky bit is, the data is scattered into many text files and, for one in particular, I couldn't find the original encoding ([*but I'm not the only one*](https://groups.google.com/g/word2vec-toolkit/c/QIUjLw6RqFk/m/_iEeyt428wkJ) 🎵). The only solution I found was to manually replace all the è, ë, ç and so on into an `utf-8` copy of the text file. I uploaded the result in my Dropbox and I am using that as the main repo for the dataset.
Also, the _sub-sentences_ are built at run-time from the information encoded in several text files, so generating the examples is a bit more cumbersome than usual. Luckily, the dataset is not enormous.
I plan to divide the dataset in 2 configs: one with just whole sentences with their labels, the other with sentences _and their sub-sentences_ with their labels. Each config will be split in train, validation and test. Hopefully this makes sense, we may discuss it in the PR I'm going to submit.
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https://api.github.com/repos/huggingface/datasets/issues/1933 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1933/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1933/comments | https://api.github.com/repos/huggingface/datasets/issues/1933/events | https://github.com/huggingface/datasets/pull/1933 | 814,335,846 | MDExOlB1bGxSZXF1ZXN0NTc4MzQwMzk3 | 1,933 | Use arrow ipc file format | {
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} | According to the [documentation](https://arrow.apache.org/docs/format/Columnar.html?highlight=arrow1#ipc-file-format), it's identical to the streaming format except that it contains the memory offsets of each sample:
> We define a “file format” supporting random access that is build with the stream format. The file starts and ends with a magic string ARROW1 (plus padding). What follows in the file is identical to the stream format. At the end of the file, we write a footer containing a redundant copy of the schema (which is a part of the streaming format) plus memory offsets and sizes for each of the data blocks in the file. This enables random access any record batch in the file. See File.fbs for the precise details of the file footer.
Since it stores more metadata regarding the positions of the examples in the file, it should enable better example retrieval performances. However from the discussion in https://github.com/huggingface/datasets/issues/1803 it looks like it's not the case unfortunately. Maybe in the future this will allow speed gains.
I think it's still a good idea to start using it anyway for these reasons:
- in the future we may have speed gains
- it contains the arrow streaming format data
- it's compatible with the pyarrow Dataset implementation (it allows to load remote dataframes for example) if we want to use it in the future
- it's also the format used by arrow feather if we want to use it in the future
- it's roughly the same size as the streaming format
- it's easy to have backward compatibility with the streaming format
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https://api.github.com/repos/huggingface/datasets/issues/1932 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1932/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1932/comments | https://api.github.com/repos/huggingface/datasets/issues/1932/events | https://github.com/huggingface/datasets/pull/1932 | 814,326,116 | MDExOlB1bGxSZXF1ZXN0NTc4MzMyMTQy | 1,932 | Fix builder config creation with data_dir | {
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} | The data_dir parameter wasn't taken into account to create the config_id, therefore the resulting builder config was considered not custom. However a builder config that is non-custom must not have a name that collides with the predefined builder config names. Therefore it resulted in a `ValueError("Cannot name a custom BuilderConfig the same as an available...")`
I fixed that by commenting the line that used to ignore the data_dir when creating the config.
It was previously ignored before the introduction of config id because we didn't want to change the config name. Now it's fine to take it into account for the config id.
Now creating a config with a data_dir works again @patrickvonplaten | {
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https://api.github.com/repos/huggingface/datasets/issues/1931 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1931/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1931/comments | https://api.github.com/repos/huggingface/datasets/issues/1931/events | https://github.com/huggingface/datasets/pull/1931 | 814,225,074 | MDExOlB1bGxSZXF1ZXN0NTc4MjQ4NTA5 | 1,931 | add m_lama (multilingual lama) dataset | {
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"Hi, it seems I am somewhat stuck here. The failed test `ci/circleci: run_dataset_script_tests_pyarrow_1_WIN` seems to be caused by some broken connection (`ConnectionResetError: [WinError 10054] An existing connection was forcibly closed by the remote host`). Any help on this is appreciated. \r\n\r\nEdit: Seems to... | 1,614,067,917,000 | 1,614,592,863,000 | 1,614,592,863,000 | CONTRIBUTOR | null | false | {
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} | Add a multilingual (machine translated and automatically generated) version of the LAMA benchmark. For details see the paper https://arxiv.org/pdf/2102.00894.pdf | {
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"Hi @JieyuZhao ! Have you had a chance to add the different configurations ?\r\nThanks again for your help on this !",
"> Hi @JieyuZhao ! Have you had a chance to add the different configurations ?\r\n> Thanks again for your help on this !\r\n\r\nHi @lhoestq Yes, I've updated the code. Now the configuration will... | 1,614,049,660,000 | 1,617,809,096,000 | 1,617,809,096,000 | CONTRIBUTOR | null | false | {
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- updated the data_url
- added different configurations for different data splits
- added the coreference_cluster to the data features | {
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https://api.github.com/repos/huggingface/datasets/issues/1929 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1929/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1929/comments | https://api.github.com/repos/huggingface/datasets/issues/1929/events | https://github.com/huggingface/datasets/pull/1929 | 813,929,669 | MDExOlB1bGxSZXF1ZXN0NTc3OTk1MTE4 | 1,929 | Improve typing and style and fix some inconsistencies | {
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"@lhoestq Thanks for the quick review.",
"I merged master to this branch to re-run the CI before merging :)"
] | 1,614,034,061,000 | 1,614,183,374,000 | 1,614,175,434,000 | CONTRIBUTOR | null | false | {
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} | This PR:
* improves typing (mostly more consistent use of `typing.Optional`)
* `DatasetDict.cleanup_cache_files` now correctly returns a dict
* replaces `dict()` with the corresponding literal
* uses `dict_to_copy.copy()` instead of `dict(dict_to_copy)` for shallow copying | {
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https://api.github.com/repos/huggingface/datasets/issues/1928 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1928/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1928/comments | https://api.github.com/repos/huggingface/datasets/issues/1928/events | https://github.com/huggingface/datasets/pull/1928 | 813,793,434 | MDExOlB1bGxSZXF1ZXN0NTc3ODgyMDM4 | 1,928 | Updating old cards | {
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} | Updated the cards for [Allocine](https://github.com/mcmillanmajora/datasets/tree/updating-old-cards/datasets/allocine), [CNN/DailyMail](https://github.com/mcmillanmajora/datasets/tree/updating-old-cards/datasets/cnn_dailymail), and [SNLI](https://github.com/mcmillanmajora/datasets/tree/updating-old-cards/datasets/snli). For the most part, the information was just rearranged or rephrased, but the social impact statements are new. | {
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https://api.github.com/repos/huggingface/datasets/issues/1927 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1927/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1927/comments | https://api.github.com/repos/huggingface/datasets/issues/1927/events | https://github.com/huggingface/datasets/pull/1927 | 813,768,935 | MDExOlB1bGxSZXF1ZXN0NTc3ODYxODM5 | 1,927 | Update README.md | {
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https://api.github.com/repos/huggingface/datasets/issues/1926 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1926/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1926/comments | https://api.github.com/repos/huggingface/datasets/issues/1926/events | https://github.com/huggingface/datasets/pull/1926 | 813,607,994 | MDExOlB1bGxSZXF1ZXN0NTc3NzI4Mjgy | 1,926 | Fix: Wiki_dpr - add missing scalar quantizer | {
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} | All the prebuilt wiki_dpr indexes already use SQ8, I forgot to update the wiki_dpr script after building them. Now it's finally done.
The scalar quantizer SQ8 doesn't reduce the performance of the index as shown in retrieval experiments on RAG.
The quantizer reduces the size of the index a lot but increases index building time. | {
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https://api.github.com/repos/huggingface/datasets/issues/1925 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1925/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1925/comments | https://api.github.com/repos/huggingface/datasets/issues/1925/events | https://github.com/huggingface/datasets/pull/1925 | 813,600,902 | MDExOlB1bGxSZXF1ZXN0NTc3NzIyMzc3 | 1,925 | Fix: Wiki_dpr - fix when with_embeddings is False or index_name is "no_index" | {
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"Hi @lhoestq ,\r\n\r\nI am running into an issue now when trying to run RAG. Running exactly as described [here](https://huggingface.co/facebook/rag-token-nq?fbclid=IwAR3bTfhls5U_t9DqsX2Vzb7NhtRHxJxfQ-uwFT7VuCPMZUM2AdAlKF_qkI8#usage) I get the error below. Wondering if it's related to this.\r\n\r\nRunning Transfor... | 1,614,007,426,000 | 1,614,216,828,000 | 1,614,008,168,000 | MEMBER | null | false | {
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} | Fix the bugs noticed in #1915
There was a bug when `with_embeddings=False` where the configuration name was the same as if `with_embeddings=True`, which led the dataset builder to do bad verifications (for example it used to expect to download the embeddings for `with_embeddings=False`).
Another issue was that setting `index_name="no_index"` didn't set `with_index` to False.
I fixed both of them and added dummy data for those configurations for testing. | {
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https://api.github.com/repos/huggingface/datasets/issues/1924 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1924/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1924/comments | https://api.github.com/repos/huggingface/datasets/issues/1924/events | https://github.com/huggingface/datasets/issues/1924 | 813,599,733 | MDU6SXNzdWU4MTM1OTk3MzM= | 1,924 | Anonymous Dataset Addition (i.e Anonymous PR?) | {
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"Hi !\r\nI guess you can add a dataset without the fields that must be kept anonymous, and then update those when the anonymity period is over.\r\nYou can also make the PR from an anonymous org.\r\nPinging @yjernite just to make sure it's ok",
"Hello,\r\nI would prefer to do the reverse: adding a link to an anony... | 1,614,007,350,000 | 1,614,104,890,000 | null | CONTRIBUTOR | null | null | null | Hello,
Thanks a lot for your librairy.
We plan to submit a paper on OpenReview using the Anonymous setting. Is it possible to add a new dataset without breaking the anonimity, with a link to the paper ?
Cheers
@eusip | {
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https://api.github.com/repos/huggingface/datasets/issues/1923 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1923/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1923/comments | https://api.github.com/repos/huggingface/datasets/issues/1923/events | https://github.com/huggingface/datasets/pull/1923 | 813,363,472 | MDExOlB1bGxSZXF1ZXN0NTc3NTI0MTU0 | 1,923 | Fix save_to_disk with relative path | {
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} | As noticed in #1919 and #1920 the target directory was not created using `makedirs` so saving to it raises `FileNotFoundError`. For absolute paths it works but not for the good reason. This is because the target path was the same as the temporary path where in-memory data are written as an intermediary step.
I added the `makedirs` call using `fs.makedirs` in order to support remote filesystems.
I also fixed the issue with the target path being the temporary path.
I added a test case for relative paths as well for save_to_disk.
Thanks to @M-Salti for reporting and investigating | {
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https://api.github.com/repos/huggingface/datasets/issues/1922 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1922/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1922/comments | https://api.github.com/repos/huggingface/datasets/issues/1922/events | https://github.com/huggingface/datasets/issues/1922 | 813,140,806 | MDU6SXNzdWU4MTMxNDA4MDY= | 1,922 | How to update the "wino_bias" dataset | {
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"Hi @JieyuZhao !\r\n\r\nYou can edit the dataset card of wino_bias to update the URL via a Pull Request. This would be really appreciated :)\r\n\r\nThe dataset card is the README.md file you can find at https://github.com/huggingface/datasets/tree/master/datasets/wino_bias\r\nAlso the homepage url is also mentioned... | 1,613,972,379,000 | 1,613,990,159,000 | null | CONTRIBUTOR | null | null | null | Hi all,
Thanks for the efforts to collect all the datasets! But I think there is a problem with the wino_bias dataset. The current link is not correct. How can I update that?
Thanks! | {
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https://api.github.com/repos/huggingface/datasets/issues/1921 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1921/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1921/comments | https://api.github.com/repos/huggingface/datasets/issues/1921/events | https://github.com/huggingface/datasets/pull/1921 | 812,716,042 | MDExOlB1bGxSZXF1ZXN0NTc3MDEzMDM4 | 1,921 | Standardizing datasets dtypes | {
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"@lhoestq - apologies for the multiple PRs, my previous one (#1905) got mangled due to some merge conflicts that I had trouble resolving so I just cherry-picked my changes onto a fresh branch here."
] | 1,613,858,641,000 | 1,613,987,050,000 | 1,613,987,050,000 | CONTRIBUTOR | null | false | {
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} | This PR follows up on discussion in #1900 to have an explicit set of basic dtypes for datasets.
This moves away from str(pyarrow.DataType) as the method of choice for creating dtypes, favoring an explicit mapping to a list of supported Value dtypes.
I believe in practice this should be backward compatible, since anyone previously using Value() would only have been able to use dtypes that had an identically named pyarrow factory function, which are all explicitly supported here, with `float32` and `float64` acting as the official datasets dtypes, which resolves the tension between `double` being the pyarrow dtype and `float64` being the pyarrow type factory function. | {
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https://api.github.com/repos/huggingface/datasets/issues/1920 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1920/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1920/comments | https://api.github.com/repos/huggingface/datasets/issues/1920/events | https://github.com/huggingface/datasets/pull/1920 | 812,628,220 | MDExOlB1bGxSZXF1ZXN0NTc2OTQ5NzI2 | 1,920 | Fix save_to_disk issue | {
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"So I was curious why the issue reported at #1919 wasn't caught in [this test](https://github.com/huggingface/datasets/blob/248104c4bdb2e01c036b7578867199191fbff181/tests/test_arrow_dataset.py#L209), so I did some digging.\r\nI tried to save to a temporary directory (just like in the test), like this:\r\n```python\... | 1,613,830,959,000 | 1,613,989,811,000 | 1,613,989,811,000 | CONTRIBUTOR | null | false | {
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https://api.github.com/repos/huggingface/datasets/issues/1919 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1919/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1919/comments | https://api.github.com/repos/huggingface/datasets/issues/1919/events | https://github.com/huggingface/datasets/issues/1919 | 812,626,872 | MDU6SXNzdWU4MTI2MjY4NzI= | 1,919 | Failure to save with save_to_disk | {
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"Hi thanks for reporting and for proposing a fix :)\r\n\r\nI just merged a fix, feel free to try it from the master branch !",
"Closing since this has been fixed by #1923"
] | 1,613,830,690,000 | 1,614,793,227,000 | 1,614,793,227,000 | CONTRIBUTOR | null | null | null | When I try to save a dataset locally using the `save_to_disk` method I get the error:
```bash
FileNotFoundError: [Errno 2] No such file or directory: '/content/squad/train/squad-train.arrow'
```
To replicate:
1. Install `datasets` from master
2. Run this code:
```python
from datasets import load_dataset
squad = load_dataset("squad") # or any other dataset
squad.save_to_disk("squad") # error here
```
The problem is that the method is not creating a directory with the name `dataset_path` for saving the dataset in (i.e. it's not creating the *train* and *validation* directories in this case). After creating the directory the problem resolves.
I'll open a PR soon doing that and linking this issue.
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https://api.github.com/repos/huggingface/datasets/issues/1918 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1918/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1918/comments | https://api.github.com/repos/huggingface/datasets/issues/1918/events | https://github.com/huggingface/datasets/pull/1918 | 812,541,510 | MDExOlB1bGxSZXF1ZXN0NTc2ODg2OTQ0 | 1,918 | Fix QA4MRE download URLs | {
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} | The URLs in the `dataset_infos` and `README` are correct, only the ones in the download script needed updating. | {
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https://api.github.com/repos/huggingface/datasets/issues/1917 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1917/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1917/comments | https://api.github.com/repos/huggingface/datasets/issues/1917/events | https://github.com/huggingface/datasets/issues/1917 | 812,390,178 | MDU6SXNzdWU4MTIzOTAxNzg= | 1,917 | UnicodeDecodeError: windows 10 machine | {
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"upgraded to php 3.9.2 and it works!"
] | 1,613,772,785,000 | 1,613,774,471,000 | 1,613,774,428,000 | NONE | null | null | null | Windows 10
Php 3.6.8
when running
```
import datasets
oscar_am = datasets.load_dataset("oscar", "unshuffled_deduplicated_am")
print(oscar_am["train"][0])
```
I get the following error
```
file "C:\PYTHON\3.6.8\lib\encodings\cp1252.py", line 23, in decode
return codecs.charmap_decode(input,self.errors,decoding_table)[0]
UnicodeDecodeError: 'charmap' codec can't decode byte 0x9d in position 58: character maps to <undefined>
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/1916 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1916/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1916/comments | https://api.github.com/repos/huggingface/datasets/issues/1916/events | https://github.com/huggingface/datasets/pull/1916 | 812,291,984 | MDExOlB1bGxSZXF1ZXN0NTc2NjgwNjY5 | 1,916 | Remove unused py_utils objects | {
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"Hmmm this one broke master. I'm fixing it.\r\n\r\nMaybe because your branch was outdated ?",
"Sorry @lhoestq, I forgot to update the imports... :/",
"It's fine, the CI should have caught this tbh. Not sure why it did't fail"
] | 1,613,764,285,000 | 1,614,005,816,000 | 1,614,000,769,000 | MEMBER | null | false | {
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https://api.github.com/repos/huggingface/datasets/issues/1915 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1915/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1915/comments | https://api.github.com/repos/huggingface/datasets/issues/1915/events | https://github.com/huggingface/datasets/issues/1915 | 812,229,654 | MDU6SXNzdWU4MTIyMjk2NTQ= | 1,915 | Unable to download `wiki_dpr` | {
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"Thanks for reporting ! This is a bug. For now feel free to set `ignore_verifications=False` in `load_dataset`.\r\nI'm working on a fix",
"I just merged a fix :)\r\n\r\nWe'll do a patch release soon. In the meantime feel free to try it from the master branch\r\nThanks again for reporting !",
"Closing since this... | 1,613,758,292,000 | 1,614,793,248,000 | 1,614,793,248,000 | NONE | null | null | null | I am trying to download the `wiki_dpr` dataset. Specifically, I want to download `psgs_w100.multiset.no_index` with no embeddings/no index. In order to do so, I ran:
`curr_dataset = load_dataset("wiki_dpr", embeddings_name="multiset", index_name="no_index")`
However, I got the following error:
`datasets.utils.info_utils.UnexpectedDownloadedFile: {'embeddings_index'}`
I tried adding in flags `with_embeddings=False` and `with_index=False`:
`curr_dataset = load_dataset("wiki_dpr", with_embeddings=False, with_index=False, embeddings_name="multiset", index_name="no_index")`
But I got the following error:
`raise ExpectedMoreDownloadedFiles(str(set(expected_checksums) - set(recorded_checksums)))
datasets.utils.info_utils.ExpectedMoreDownloadedFiles: {‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_5’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_15’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_30’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_36’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_18’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_41’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_13’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_48’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_10’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_23’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_14’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_34’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_43’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_40’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_47’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_3’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_24’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_7’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_33’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_46’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_42’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_27’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_29’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_26’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_22’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_4’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_20’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_39’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_6’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_16’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_8’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_35’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_49’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_17’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_25’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_0’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_38’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_12’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_44’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_1’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_32’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_19’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_31’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_37’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_9’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_11’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_21’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_28’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_45’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_2’}`
Is there anything else I need to set to download the dataset?
**UPDATE**: just running `curr_dataset = load_dataset("wiki_dpr", with_embeddings=False, with_index=False)` gives me the same error.
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https://api.github.com/repos/huggingface/datasets/issues/1913 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1913/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1913/comments | https://api.github.com/repos/huggingface/datasets/issues/1913/events | https://github.com/huggingface/datasets/pull/1913 | 812,127,307 | MDExOlB1bGxSZXF1ZXN0NTc2NTQ0NjQw | 1,913 | Add keep_linebreaks parameter to text loader | {
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"Just so I understand how it can be used in practice, do you have an example showing how to load a text dataset with this option?",
"Sure ! Here is an example:\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nload_dataset(\"text\", keep_linebreaks=True, data_files=...)\r\n```\r\n\r\nI'll update the docume... | 1,613,749,425,000 | 1,613,759,772,000 | 1,613,759,771,000 | MEMBER | null | false | {
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} | As asked in #870 and https://github.com/huggingface/transformers/issues/10269 there should be a parameter to keep the linebreaks when loading a text dataset.
cc @sgugger @jncasey | {
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https://api.github.com/repos/huggingface/datasets/issues/1912 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1912/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1912/comments | https://api.github.com/repos/huggingface/datasets/issues/1912/events | https://github.com/huggingface/datasets/pull/1912 | 812,034,140 | MDExOlB1bGxSZXF1ZXN0NTc2NDY2ODQx | 1,912 | Update: WMT - use mirror links | {
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"So much better - thank you for doing that, @lhoestq!",
"Also fixed the `uncorpus` urls for wmt19 ru-en and zh-en for https://github.com/huggingface/datasets/issues/1893",
"Thanks!\r\nCan this be merged sooner? \r\nI manually update it and it works well."
] | 1,613,742,154,000 | 1,614,174,293,000 | 1,614,174,293,000 | MEMBER | null | false | {
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} | As asked in #1892 I created mirrors of the data hosted on statmt.org and updated the wmt scripts.
Now downloading the wmt datasets is blazing fast :)
cc @stas00 @patrickvonplaten | {
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https://api.github.com/repos/huggingface/datasets/issues/1911 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1911/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1911/comments | https://api.github.com/repos/huggingface/datasets/issues/1911/events | https://github.com/huggingface/datasets/issues/1911 | 812,009,956 | MDU6SXNzdWU4MTIwMDk5NTY= | 1,911 | Saving processed dataset running infinitely | {
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"@thomwolf @lhoestq can you guys please take a look and recommend some solution.",
"am suspicious of this thing? what's the purpose of this? pickling and unplickling\r\n`self = pickle.loads(pickle.dumps(self))`\r\n\r\n```\r\n def save_to_disk(self, dataset_path: str, fs=None):\r\n \"\"\"\r\n Save... | 1,613,740,159,000 | 1,614,065,684,000 | null | NONE | null | null | null | I have a text dataset of size 220M.
For pre-processing, I need to tokenize this and filter rows with the large sequence.
My tokenization took roughly 3hrs. I used map() with batch size 1024 and multi-process with 96 processes.
filter() function was way to slow, so I used a hack to use pyarrow filter table function, which is damm fast. Mentioned [here](https://github.com/huggingface/datasets/issues/1796)
```dataset._data = dataset._data.filter(...)```
It took 1 hr for the filter.
Then i use `save_to_disk()` on processed dataset and it is running forever.
I have been waiting since 8 hrs, it has not written a single byte.
Infact it has actually read from disk more than 100GB, screenshot below shows the stats using `iotop`.
Second process is the one.
<img width="1672" alt="Screenshot 2021-02-19 at 6 36 53 PM" src="https://user-images.githubusercontent.com/20911334/108508197-7325d780-72e1-11eb-8369-7c057d137d81.png">
I am not able to figure out, whether this is some issue with dataset library or that it is due to my hack for filter() function. | {
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https://api.github.com/repos/huggingface/datasets/issues/1910 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1910/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1910/comments | https://api.github.com/repos/huggingface/datasets/issues/1910/events | https://github.com/huggingface/datasets/pull/1910 | 811,697,108 | MDExOlB1bGxSZXF1ZXN0NTc2MTg0MDQ3 | 1,910 | Adding CoNLLpp dataset. | {
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"It looks like this PR now includes changes to many other files than the ones for CoNLLpp.\r\n\r\nTo fix that feel free to create another branch and another PR.\r\n\r\nThis was probably caused by a git rebase. You can avoid this issue by using git merge if you've already pushed your branch."
] | 1,613,711,550,000 | 1,614,895,367,000 | 1,614,895,367,000 | CONTRIBUTOR | null | false | {
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https://api.github.com/repos/huggingface/datasets/issues/1907 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1907/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1907/comments | https://api.github.com/repos/huggingface/datasets/issues/1907/events | https://github.com/huggingface/datasets/issues/1907 | 811,520,569 | MDU6SXNzdWU4MTE1MjA1Njk= | 1,907 | DBPedia14 Dataset Checksum bug? | {
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"Hi ! :)\r\n\r\nThis looks like the same issue as https://github.com/huggingface/datasets/issues/1856 \r\nBasically google drive has quota issues that makes it inconvenient for downloading files.\r\n\r\nIf the quota of a file is exceeded, you have to wait 24h for the quota to reset (which is painful).\r\n\r\nThe er... | 1,613,687,148,000 | 1,614,036,125,000 | 1,614,036,124,000 | CONTRIBUTOR | null | null | null | Hi there!!!
I've been using successfully the DBPedia dataset (https://huggingface.co/datasets/dbpedia_14) with my codebase in the last couple of weeks, but in the last couple of days now I get this error:
```
Traceback (most recent call last):
File "./conditional_classification/basic_pipeline.py", line 178, in <module>
main()
File "./conditional_classification/basic_pipeline.py", line 128, in main
corpus.load_data(limit_train_examples_per_class=args.data_args.train_examples_per_class,
File "/home/fp/dev/conditional_classification/conditional_classification/datasets_base.py", line 83, in load_data
datasets = load_dataset(self.name, split=dataset_split)
File "/home/fp/anaconda3/envs/conditional/lib/python3.8/site-packages/datasets/load.py", line 609, in load_dataset
builder_instance.download_and_prepare(
File "/home/fp/anaconda3/envs/conditional/lib/python3.8/site-packages/datasets/builder.py", line 526, in download_and_prepare
self._download_and_prepare(
File "/home/fp/anaconda3/envs/conditional/lib/python3.8/site-packages/datasets/builder.py", line 586, in _download_and_prepare
verify_checksums(
File "/home/fp/anaconda3/envs/conditional/lib/python3.8/site-packages/datasets/utils/info_utils.py", line 39, in verify_checksums
raise NonMatchingChecksumError(error_msg + str(bad_urls))
datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:
['https://drive.google.com/uc?export=download&id=0Bz8a_Dbh9QhbQ2Vic1kxMmZZQ1k']
```
I've seen this has happened before in other datasets as reported in #537.
I've tried clearing my cache and call again `load_dataset` but still is not working. My same codebase is successfully downloading and using other datasets (e.g. AGNews) without any problem, so I guess something has happened specifically to the DBPedia dataset in the last few days.
Can you please check if there's a problem with the checksums?
Or this is related to any other stuff? I've seen that the path in the cache for the dataset is `/home/fp/.cache/huggingface/datasets/d_bpedia14/dbpedia_14/2.0.0/a70413e39e7a716afd0e90c9e53cb053691f56f9ef5fe317bd07f2c368e8e897...` and includes `d_bpedia14` instead maybe of `dbpedia_14`. Was this maybe a bug introduced recently?
Thanks! | {
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https://api.github.com/repos/huggingface/datasets/issues/1906 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1906/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1906/comments | https://api.github.com/repos/huggingface/datasets/issues/1906/events | https://github.com/huggingface/datasets/issues/1906 | 811,405,274 | MDU6SXNzdWU4MTE0MDUyNzQ= | 1,906 | Feature Request: Support for Pandas `Categorical` | {
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"We already have a ClassLabel type that does this kind of mapping between the label ids (integers) and actual label values (strings).\r\n\r\nI wonder if actually we should use the DictionaryType from Arrow and the Categorical type from pandas for the `datasets` ClassLabel feature type.\r\nCurrently ClassLabel corre... | 1,613,677,565,000 | 1,614,091,130,000 | null | CONTRIBUTOR | null | null | null | ```
from datasets import Dataset
import pandas as pd
import pyarrow
df = pd.DataFrame(pd.Series(["a", "b", "c", "a"], dtype="category"))
pyarrow.Table.from_pandas(df)
Dataset.from_pandas(df)
# Throws NotImplementedError
# TODO(thom) this will need access to the dictionary as well (for labels). I.e. to the py_table
```
I'm curious if https://github.com/huggingface/datasets/blob/master/src/datasets/features.py#L796 could be built out in a way similar to `Sequence`?
e.g. a `Map` class (or whatever name the maintainers might prefer) that can accept:
```
index_type = generate_from_arrow_type(pa_type.index_type)
value_type = generate_from_arrow_type(pa_type.value_type)
```
and then additional code points to modify:
- FeatureType: https://github.com/huggingface/datasets/blob/master/src/datasets/features.py#L694
- A branch to handle Map in get_nested_type: https://github.com/huggingface/datasets/blob/master/src/datasets/features.py#L719
- I don't quite understand what `encode_nested_example` does but perhaps a branch there? https://github.com/huggingface/datasets/blob/master/src/datasets/features.py#L755
- Similarly, I don't quite understand why `Sequence` is used this way in `generate_from_dict`, but perhaps a branch here? https://github.com/huggingface/datasets/blob/master/src/datasets/features.py#L775
I couldn't find other usages of `Sequence` outside of defining specific datasets, so I'm not sure if that's a comprehensive set of touchpoints. | {
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https://api.github.com/repos/huggingface/datasets/issues/1905 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1905/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1905/comments | https://api.github.com/repos/huggingface/datasets/issues/1905/events | https://github.com/huggingface/datasets/pull/1905 | 811,384,174 | MDExOlB1bGxSZXF1ZXN0NTc1OTIxMDk1 | 1,905 | Standardizing datasets.dtypes | {
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"Also - I took a stab at updating the docs, but I'm not sure how to actually check the outputs to see if it's formatted properly."
] | 1,613,675,731,000 | 1,613,858,490,000 | 1,613,858,490,000 | CONTRIBUTOR | null | true | {
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} | This PR was further branched off of jdy-str-to-pyarrow-parsing, so it depends on https://github.com/huggingface/datasets/pull/1900 going first for the diff to be up-to-date (I'm not sure if there's a way for me to use jdy-str-to-pyarrow-parsing as a base branch while having it appear in the pull requests here).
This moves away from `str(pyarrow.DataType)` as the method of choice for creating dtypes, favoring an explicit mapping to a list of supported Value dtypes.
I believe in practice this should be backward compatible, since anyone previously using Value() would only have been able to use dtypes that had an identically named pyarrow factory function, which are all explicitly supported here. | {
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https://api.github.com/repos/huggingface/datasets/issues/1904 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1904/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1904/comments | https://api.github.com/repos/huggingface/datasets/issues/1904/events | https://github.com/huggingface/datasets/pull/1904 | 811,260,904 | MDExOlB1bGxSZXF1ZXN0NTc1ODE4MjA0 | 1,904 | Fix to_pandas for boolean ArrayXD | {
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"Thanks!"
] | 1,613,665,846,000 | 1,613,668,203,000 | 1,613,668,201,000 | MEMBER | null | false | {
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} | As noticed in #1887 the conversion of a dataset with a boolean ArrayXD feature types fails because of the underlying ListArray conversion to numpy requires `zero_copy_only=False`.
zero copy is available for all primitive types except booleans
see https://arrow.apache.org/docs/python/generated/pyarrow.Array.html#pyarrow.Array.to_numpy
and https://issues.apache.org/jira/browse/ARROW-2871?jql=text%20~%20%22boolean%20to_numpy%22
cc @SBrandeis | {
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https://api.github.com/repos/huggingface/datasets/issues/1903 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1903/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1903/comments | https://api.github.com/repos/huggingface/datasets/issues/1903/events | https://github.com/huggingface/datasets/pull/1903 | 811,145,531 | MDExOlB1bGxSZXF1ZXN0NTc1NzIwOTk2 | 1,903 | Initial commit for the addition of TIMIT dataset | {
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"@patrickvonplaten could you please review and help me close this PR?",
"@lhoestq Thank you so much for your comments and for patiently reviewing the code. Have _hopefully_ included all the suggested changes. Let me know if any more changes are required.\r\n\r\nSorry the code had lots of silly errors from my sid... | 1,613,658,192,000 | 1,614,591,552,000 | 1,614,591,552,000 | CONTRIBUTOR | null | false | {
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} | Below points needs to be addressed:
- Creation of dummy dataset is failing
- Need to check on the data representation
- License is not creative commons. Copyright: Portions © 1993 Trustees of the University of Pennsylvania
Also the links (_except the download_) point to the ami corpus! ;-)
@patrickvonplaten Requesting your comments, will be happy to address them! | {
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https://api.github.com/repos/huggingface/datasets/issues/1902 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1902/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1902/comments | https://api.github.com/repos/huggingface/datasets/issues/1902/events | https://github.com/huggingface/datasets/pull/1902 | 810,931,171 | MDExOlB1bGxSZXF1ZXN0NTc1NTQwMDM1 | 1,902 | Fix setimes_2 wmt urls | {
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Some other urls were missing https | {
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} | Replace http to https.
https://github.com/huggingface/datasets/issues/854
https://discuss.huggingface.co/t/cannot-download-wmt16/2081
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https://api.github.com/repos/huggingface/datasets/issues/1900 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1900/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1900/comments | https://api.github.com/repos/huggingface/datasets/issues/1900/events | https://github.com/huggingface/datasets/pull/1900 | 810,512,488 | MDExOlB1bGxSZXF1ZXN0NTc1MTkxNTc3 | 1,900 | Issue #1895: Bugfix for string_to_arrow timestamp[ns] support | {
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"OK! Thank you for the review - I will follow up with a separate PR for the comments here (https://github.com/huggingface/datasets/pull/1900#discussion_r578319725)!"
] | 1,613,593,564,000 | 1,613,759,231,000 | 1,613,759,231,000 | CONTRIBUTOR | null | false | {
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} | Should resolve https://github.com/huggingface/datasets/issues/1895
The main part of this PR adds additional parsing in `string_to_arrow` to convert the timestamp dtypes that result from `str(pa_type)` back into the pa.DataType TimestampType.
While adding unit-testing, I noticed that support for the double/float types also don't invert correctly, so I added them, which I believe would hypothetically make this section of `Value` redundant:
```
def __post_init__(self):
if self.dtype == "double": # fix inferred type
self.dtype = "float64"
if self.dtype == "float": # fix inferred type
self.dtype = "float32"
```
However, since I think Value.dtype is part of the public interface, removing that would result in a backward-incompatible change, so I didn't muck with that.
The rest of the PR consists of docstrings that I added while developing locally so I could keep track of which functions were supposed to be inverses of each other, and thought I'd include them initially in case you want to keep them around, but I'm happy to delete or remove any of them at your request! | {
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https://api.github.com/repos/huggingface/datasets/issues/1899 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1899/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1899/comments | https://api.github.com/repos/huggingface/datasets/issues/1899/events | https://github.com/huggingface/datasets/pull/1899 | 810,308,332 | MDExOlB1bGxSZXF1ZXN0NTc1MDIxMjc4 | 1,899 | Fix: ALT - fix duplicated examples in alt-parallel | {
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} | As noticed in #1898 by @10-zin the examples of the `alt-paralel` configurations have all the same values for the `translation` field.
This was due to a bad copy of a python dict.
This PR fixes that. | {
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"Thanks for reporting. This looks like a very bad issue. I'm looking into it",
"I just merged a fix, we'll do a patch release soon. Thanks again for reporting, and sorry for the inconvenience.\r\nIn the meantime you can load `ALT` using `datasets` from the master branch",
"Thanks!!! works perfectly in the blead... | 1,613,566,302,000 | 1,613,715,526,000 | 1,613,715,526,000 | NONE | null | null | null | The [ALT](https://huggingface.co/datasets/alt) dataset has all the same instances within each split :/
Seemed like a great dataset for some experiments I wanted to carry out, especially since its medium-sized, and has all splits.
Would be great if this could be fixed :)
Added a snapshot of the contents from `explore-datset` feature, for quick reference.
| {
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https://api.github.com/repos/huggingface/datasets/issues/1897 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1897/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1897/comments | https://api.github.com/repos/huggingface/datasets/issues/1897/events | https://github.com/huggingface/datasets/pull/1897 | 810,113,263 | MDExOlB1bGxSZXF1ZXN0NTc0ODU3MTIy | 1,897 | Fix PandasArrayExtensionArray conversion to native type | {
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} | [] | closed | false | null | [] | null | [] | 1,613,562,504,000 | 1,613,567,716,000 | 1,613,567,715,000 | MEMBER | null | false | {
"url": "https://api.github.com/repos/huggingface/datasets/pulls/1897",
"html_url": "https://github.com/huggingface/datasets/pull/1897",
"diff_url": "https://github.com/huggingface/datasets/pull/1897.diff",
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"merged_at": 1613567715000
} | To make the conversion to csv work in #1887 , we need PandasArrayExtensionArray used for multidimensional numpy arrays to be converted to pandas native types.
However previously pandas.core.internals.ExtensionBlock.to_native_types would fail with an PandasExtensionArray because
1. the PandasExtensionArray.isna method was wrong
2. the conversion of a PandasExtensionArray to a numpy array with dtype=object was returning a multidimensional array while pandas excepts a 1D array in this case (more info [here](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.api.extensions.ExtensionArray.html#pandas.api.extensions.ExtensionArray))
I fixed these two issues and now the conversion to native types works, and so is the export to csv.
cc @SBrandeis | {
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https://api.github.com/repos/huggingface/datasets/issues/1895 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1895/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1895/comments | https://api.github.com/repos/huggingface/datasets/issues/1895/events | https://github.com/huggingface/datasets/issues/1895 | 809,630,271 | MDU6SXNzdWU4MDk2MzAyNzE= | 1,895 | Bug Report: timestamp[ns] not recognized | {
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"Thanks for reporting !\r\n\r\nYou're right, `string_to_arrow` should be able to take `\"timestamp[ns]\"` as input and return the right pyarrow timestamp type.\r\nFeel free to suggest a fix for `string_to_arrow` and open a PR if you want to contribute ! This would be very appreciated :)\r\n\r\nTo give you more cont... | 1,613,507,884,000 | 1,613,759,231,000 | 1,613,759,231,000 | CONTRIBUTOR | null | null | null | Repro:
```
from datasets import Dataset
import pandas as pd
import pyarrow
df = pd.DataFrame(pd.date_range("2018-01-01", periods=3, freq="H"))
pyarrow.Table.from_pandas(df)
Dataset.from_pandas(df)
# Throws ValueError: Neither timestamp[ns] nor timestamp[ns]_ seems to be a pyarrow data type.
```
The factory function seems to be just "timestamp": https://arrow.apache.org/docs/python/generated/pyarrow.timestamp.html#pyarrow.timestamp
It seems like https://github.com/huggingface/datasets/blob/master/src/datasets/features.py#L36-L43 could have a little bit of additional structure for handling these cases? I'd be happy to take a shot at opening a PR if I could receive some guidance on whether parsing something like `timestamp[ns]` and resolving it to timestamp('ns') is the goal of this method.
Alternatively, if I'm using this incorrectly (e.g. is the expectation that we always provide a schema when timestamps are involved?), that would be very helpful to know as well!
```
$ pip list # only the relevant libraries/versions
datasets 1.2.1
pandas 1.0.3
pyarrow 3.0.0
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/1894 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1894/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1894/comments | https://api.github.com/repos/huggingface/datasets/issues/1894/events | https://github.com/huggingface/datasets/issues/1894 | 809,609,654 | MDU6SXNzdWU4MDk2MDk2NTQ= | 1,894 | benchmarking against MMapIndexedDataset | {
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"Hi sam !\r\nIndeed we can expect the performances to be very close since both MMapIndexedDataset and the `datasets` implem use memory mapping. With memory mapping what determines the I/O performance is the speed of your hard drive/SSD.\r\n\r\nIn terms of performance we're pretty close to the optimal speed for read... | 1,613,505,898,000 | 1,613,587,948,000 | null | CONTRIBUTOR | null | null | null | I am trying to benchmark my datasets based implementation against fairseq's [`MMapIndexedDataset`](https://github.com/pytorch/fairseq/blob/master/fairseq/data/indexed_dataset.py#L365) and finding that, according to psrecord, my `datasets` implem uses about 3% more CPU memory and runs 1% slower for `wikitext103` (~1GB of tokens).
Questions:
1) Is this (basically identical) performance expected?
2) Is there a scenario where this library will outperform `MMapIndexedDataset`? (maybe more examples/larger examples?)
3) Should I be using different benchmarking tools than `psrecord`/how do you guys do benchmarks?
Thanks in advance! Sam | {
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https://api.github.com/repos/huggingface/datasets/issues/1893 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1893/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1893/comments | https://api.github.com/repos/huggingface/datasets/issues/1893/events | https://github.com/huggingface/datasets/issues/1893 | 809,556,503 | MDU6SXNzdWU4MDk1NTY1MDM= | 1,893 | wmt19 is broken | {
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"This was also mentioned in https://github.com/huggingface/datasets/issues/488 \r\n\r\nThe bucket where is data was stored seems to be unavailable now. Maybe we can change the URL to the ones in https://conferences.unite.un.org/uncorpus/en/downloadoverview ?",
"Closing since this has been fixed by #1912"
] | 1,613,500,798,000 | 1,614,793,322,000 | 1,614,793,322,000 | CONTRIBUTOR | null | null | null | 1. Check which lang pairs we have: `--dataset_name wmt19`:
Please pick one among the available configs: ['cs-en', 'de-en', 'fi-en', 'gu-en', 'kk-en', 'lt-en', 'ru-en', 'zh-en', 'fr-de']
2. OK, let's pick `ru-en`:
`--dataset_name wmt19 --dataset_config "ru-en"`
no cookies:
```
Traceback (most recent call last):
File "./run_seq2seq.py", line 661, in <module>
main()
File "./run_seq2seq.py", line 317, in main
datasets = load_dataset(data_args.dataset_name, data_args.dataset_config_name)
File "/mnt/nvme1/code/huggingface/datasets-master/src/datasets/load.py", line 740, in load_dataset
builder_instance.download_and_prepare(
File "/mnt/nvme1/code/huggingface/datasets-master/src/datasets/builder.py", line 572, in download_and_prepare
self._download_and_prepare(
File "/mnt/nvme1/code/huggingface/datasets-master/src/datasets/builder.py", line 628, in _download_and_prepare
split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
File "/home/stas/.cache/huggingface/modules/datasets_modules/datasets/wmt19/436092de5f3faaf0fc28bc84875475b384e90a5470fa6afaee11039ceddc5052/wmt_utils.py", line 755, in _split_generators
downloaded_files = dl_manager.download_and_extract(urls_to_download)
File "/mnt/nvme1/code/huggingface/datasets-master/src/datasets/utils/download_manager.py", line 276, in download_and_extract
return self.extract(self.download(url_or_urls))
File "/mnt/nvme1/code/huggingface/datasets-master/src/datasets/utils/download_manager.py", line 191, in download
downloaded_path_or_paths = map_nested(
File "/mnt/nvme1/code/huggingface/datasets-master/src/datasets/utils/py_utils.py", line 233, in map_nested
mapped = [
File "/mnt/nvme1/code/huggingface/datasets-master/src/datasets/utils/py_utils.py", line 234, in <listcomp>
_single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)
File "/mnt/nvme1/code/huggingface/datasets-master/src/datasets/utils/py_utils.py", line 190, in _single_map_nested
mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar]
File "/mnt/nvme1/code/huggingface/datasets-master/src/datasets/utils/py_utils.py", line 190, in <listcomp>
mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar]
File "/mnt/nvme1/code/huggingface/datasets-master/src/datasets/utils/py_utils.py", line 172, in _single_map_nested
return function(data_struct)
File "/mnt/nvme1/code/huggingface/datasets-master/src/datasets/utils/download_manager.py", line 211, in _download
return cached_path(url_or_filename, download_config=download_config)
File "/mnt/nvme1/code/huggingface/datasets-master/src/datasets/utils/file_utils.py", line 274, in cached_path
output_path = get_from_cache(
File "/mnt/nvme1/code/huggingface/datasets-master/src/datasets/utils/file_utils.py", line 584, in get_from_cache
raise FileNotFoundError("Couldn't find file at {}".format(url))
FileNotFoundError: Couldn't find file at https://storage.googleapis.com/tfdataset-data/downloadataset/uncorpus/UNv1.0.en-ru.tar.gz
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/1892 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1892/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1892/comments | https://api.github.com/repos/huggingface/datasets/issues/1892/events | https://github.com/huggingface/datasets/issues/1892 | 809,554,174 | MDU6SXNzdWU4MDk1NTQxNzQ= | 1,892 | request to mirror wmt datasets, as they are really slow to download | {
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"Yes that would be awesome. Not only the download speeds are awful, but also some files are missing.\r\nWe list all the URLs in the datasets/wmt19/wmt_utils.py so we can make a script to download them all and host on S3.\r\nAlso I think most of the materials are under the CC BY-NC-SA 3.0 license (must double check)... | 1,613,500,571,000 | 1,635,231,342,000 | 1,616,673,203,000 | CONTRIBUTOR | null | null | null | Would it be possible to mirror the wmt data files under hf? Some of them take hours to download and not because of the local speed. They are all quite small datasets, just extremely slow to download.
Thank you! | {
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https://api.github.com/repos/huggingface/datasets/issues/1891 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1891/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1891/comments | https://api.github.com/repos/huggingface/datasets/issues/1891/events | https://github.com/huggingface/datasets/issues/1891 | 809,550,001 | MDU6SXNzdWU4MDk1NTAwMDE= | 1,891 | suggestion to improve a missing dataset error | {
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} | [] | open | false | null | [] | null | [] | 1,613,500,153,000 | 1,613,500,214,000 | null | CONTRIBUTOR | null | null | null | I was using `--dataset_name wmt19` all was good. Then thought perhaps wmt20 is out, so I tried to use `--dataset_name wmt20`, got 3 different errors (1 repeated twice), none telling me the real issue - that `wmt20` isn't in the `datasets`:
```
True, predict_with_generate=True)
Traceback (most recent call last):
File "/mnt/nvme1/code/huggingface/datasets-master/src/datasets/load.py", line 323, in prepare_module
local_path = cached_path(file_path, download_config=download_config)
File "/mnt/nvme1/code/huggingface/datasets-master/src/datasets/utils/file_utils.py", line 274, in cached_path
output_path = get_from_cache(
File "/mnt/nvme1/code/huggingface/datasets-master/src/datasets/utils/file_utils.py", line 584, in get_from_cache
raise FileNotFoundError("Couldn't find file at {}".format(url))
FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/huggingface/datasets/master/datasets/wmt20/wmt20.py
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/mnt/nvme1/code/huggingface/datasets-master/src/datasets/load.py", line 335, in prepare_module
local_path = cached_path(file_path, download_config=download_config)
File "/mnt/nvme1/code/huggingface/datasets-master/src/datasets/utils/file_utils.py", line 274, in cached_path
output_path = get_from_cache(
File "/mnt/nvme1/code/huggingface/datasets-master/src/datasets/utils/file_utils.py", line 584, in get_from_cache
raise FileNotFoundError("Couldn't find file at {}".format(url))
FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/huggingface/datasets/master/datasets/wmt20/wmt20.py
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "./run_seq2seq.py", line 661, in <module>
main()
File "./run_seq2seq.py", line 317, in main
datasets = load_dataset(data_args.dataset_name, data_args.dataset_config_name)
File "/mnt/nvme1/code/huggingface/datasets-master/src/datasets/load.py", line 706, in load_dataset
module_path, hash, resolved_file_path = prepare_module(
File "/mnt/nvme1/code/huggingface/datasets-master/src/datasets/load.py", line 343, in prepare_module
raise FileNotFoundError(
FileNotFoundError: Couldn't find file locally at wmt20/wmt20.py, or remotely at https://raw.githubusercontent.com/huggingface/datasets/master/datasets/wmt20/wmt20.py.
The file is also not present on the master branch on github.
```
Suggestion: if it is not in a local path, check that there is an actual `https://github.com/huggingface/datasets/tree/master/datasets/wmt20` first and assert "dataset `wmt20` doesn't exist in datasets", rather than trying to find a load script - since the whole repo is not there.
The error occured when running:
```
cd examples/seq2seq
export BS=16; rm -r output_dir; PYTHONPATH=../../src USE_TF=0 CUDA_VISIBLE_DEVICES=0 python ./run_seq2seq.py --model_name_or_path t5-small --output_dir output_dir --adam_eps 1e-06 --do_eval --evaluation_strategy=steps --label_smoothing 0.1 --learning_rate 3e-5 --logging_first_step --logging_steps 1000 --max_source_length 128 --max_target_length 128 --num_train_epochs 1 --overwrite_output_dir --per_device_eval_batch_size $BS --predict_with_generate --eval_steps 25000 --sortish_sampler --task translation_en_to_ro --val_max_target_length 128 --warmup_steps 500 --max_val_samples 500 --dataset_name wmt20 --dataset_config "ro-en" --source_prefix "translate English to Romanian: "
```
Thanks. | {
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"Next step is going to add these two in the documentation ^^"
] | 1,613,479,099,000 | 1,613,673,757,000 | 1,613,673,754,000 | CONTRIBUTOR | null | false | {
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https://api.github.com/repos/huggingface/datasets/issues/1888 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1888/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1888/comments | https://api.github.com/repos/huggingface/datasets/issues/1888/events | https://github.com/huggingface/datasets/pull/1888 | 809,241,123 | MDExOlB1bGxSZXF1ZXN0NTc0MTM2MDU4 | 1,888 | Docs for adding new column on formatted dataset | {
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"Close #1872"
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Close #1872 | {
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"@lhoestq I stumbled upon an interesting failure when adding tests for CSV serialization of `ArrayXD` features (see the failing unit tests in the CI)\r\n\r\nIt's due to the fact that booleans cannot be converted from arrow format to numpy without copy: https://arrow.apache.org/docs/python/generated/pyarrow.Array.ht... | 1,613,474,849,000 | 1,613,727,719,000 | 1,613,727,719,000 | CONTRIBUTOR | null | false | {
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} | cc @thomwolf
`to_csv` supports passing either a file path or a *binary* file object
The writing is batched to avoid loading the whole table in memory | {
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"Does it make sense to make the domains as the different languages?\r\nA problem is that you need to download the datasets from the browser.\r\nOne idea would be to either contact Mozilla regarding API access to the dataset or make use of a headless browser for downloading the datasets (might be hard since we have ... | 1,613,474,170,000 | 1,615,315,891,000 | 1,615,315,891,000 | CONTRIBUTOR | null | false | {
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} | Started filling out information about the dataset and a dataset card.
To do
Create tagging file
Update the common_voice.py file with more information | {
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@lhoestq | {
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"@lhoestq I am not sure how to test `dictionary_encode_column` (in-place version was not tested before)",
"I can take a look at dictionary_encode_column tomorrow.\r\nAlthough it's likely that it doesn't work then. It was added at the beginning of the lib and never tested nor used afaik.",
"Now let's update the ... | 1,613,414,666,000 | 1,614,178,489,000 | 1,614,178,406,000 | CONTRIBUTOR | null | false | {
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"@lhoestq I have refactorized the logic. Instead of the previous hierarchy call (local temp file opening -> remote call -> use again temp local file logic but from within the remote caller scope), now it is flattened. Schematically:\r\n```python\r\nwith src.open() as src_file, dst.open() as dst_file:\r\n src_fil... | 1,613,410,584,000 | 1,615,220,110,000 | null | MEMBER | null | false | {
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https://api.github.com/repos/huggingface/datasets/issues/1881 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1881/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1881/comments | https://api.github.com/repos/huggingface/datasets/issues/1881/events | https://github.com/huggingface/datasets/pull/1881 | 808,578,200 | MDExOlB1bGxSZXF1ZXN0NTczNTk1Nzkw | 1,881 | `list_datasets()` returns a list of strings, not objects | {
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} | Here and there in the docs there is still stuff like this:
```python
>>> datasets_list = list_datasets()
>>> print(', '.join(dataset.id for dataset in datasets_list))
```
However, my understanding is that `list_datasets()` returns a list of strings rather than a list of objects. | {
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https://api.github.com/repos/huggingface/datasets/issues/1880 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1880/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1880/comments | https://api.github.com/repos/huggingface/datasets/issues/1880/events | https://github.com/huggingface/datasets/pull/1880 | 808,563,439 | MDExOlB1bGxSZXF1ZXN0NTczNTgzNjg0 | 1,880 | Update multi_woz_v22 checksums | {
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I updated them in this PR | {
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"Hi @lhoestq. If you agree to merge this, I will start separating the logic for NestedDataStructure.map ;)"
] | 1,613,395,780,000 | 1,613,759,714,000 | 1,613,759,714,000 | MEMBER | null | false | {
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} | Replace `flatten_nested` with `NestedDataStructure.flatten`.
This is a first step towards having all NestedDataStructure logic as a separated concern, independent of the caller/user of the data structure.
Eventually, all checks (whether the underlying data is list, dict, etc.) will be only inside this class.
I have also generalized the flattening, and now it handles multiple levels of nesting. | {
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https://api.github.com/repos/huggingface/datasets/issues/1878 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1878/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1878/comments | https://api.github.com/repos/huggingface/datasets/issues/1878/events | https://github.com/huggingface/datasets/pull/1878 | 808,526,883 | MDExOlB1bGxSZXF1ZXN0NTczNTUyODk3 | 1,878 | Add LJ Speech dataset | {
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"Hey @anton-l,\r\n\r\nThanks a lot for the very clean integration!\r\n\r\n1) I think we should now start having \"automatic-speech-recognition\" as a label in the dataset tagger (@yjernite is it easy to add?). But we can surely add this dataset with the tag you've added and then later change the label to `asr` \r\n... | 1,613,394,642,000 | 1,613,417,981,000 | 1,613,398,689,000 | CONTRIBUTOR | null | false | {
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} | This PR adds the LJ Speech dataset (https://keithito.com/LJ-Speech-Dataset/)
As requested by #1841
The ASR format is based on #1767
There are a couple of quirks that should be addressed:
- I tagged this dataset as `other-other-automatic-speech-recognition` and `other-other-text-to-speech` (as classified by paperswithcode). Since the number of speech datasets is about to grow, maybe these categories should be added to the main list?
- Similarly to #1767 this dataset uses only a single dummy sample to reduce the zip size (`wav`s are quite heavy). Is there a plan to allow LFS or S3 usage for dummy data in the repo?
- The dataset is distributed under the Public Domain license, which is not used anywhere else in the repo, AFAIK. Do you think Public Domain is worth adding to the tagger app as well?
Pinging @patrickvonplaten to review | {
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https://api.github.com/repos/huggingface/datasets/issues/1877 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1877/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1877/comments | https://api.github.com/repos/huggingface/datasets/issues/1877/events | https://github.com/huggingface/datasets/issues/1877 | 808,462,272 | MDU6SXNzdWU4MDg0NjIyNzI= | 1,877 | Allow concatenation of both in-memory and on-disk datasets | {
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"I started working on this. My idea is to first add the pyarrow Table wrappers InMemoryTable and MemoryMappedTable that both implement what's necessary regarding copy/pickle. Then have another wrapper that takes the concatenation of InMemoryTable/MemoryMappedTable objects.\r\n\r\nWhat's important here is that conca... | 1,613,389,186,000 | 1,616,777,518,000 | 1,616,777,518,000 | MEMBER | null | null | null | This is a prerequisite for the addition of the `add_item` feature (see #1870).
Currently there is one assumption that we would need to change: a dataset is either fully in memory (dataset._data_files is empty), or the dataset can be reloaded from disk (using the dataset._data_files).
This assumption is used for pickling for example:
- in-memory dataset can just be pickled/unpickled in-memory
- on-disk dataset can be unloaded to only keep the filepaths when pickling, and then reloaded from the disk when unpickling
Maybe let's have a design that allows a Dataset to have a Table that can be rebuilt from heterogenous sources like in-memory tables or on-disk tables ? This could also be further extended in the future
One idea would be to define a list of sources and each source implements a way to reload its corresponding pyarrow Table.
Then the dataset would be the concatenation of all these tables.
Depending on the source type, the serialization using pickle would be different. In-memory data would be copied while on-disk data would simply be replaced by the path to these data.
If you have some ideas you would like to share about the design/API feel free to do so :)
cc @albertvillanova | {
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https://api.github.com/repos/huggingface/datasets/issues/1876 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1876/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1876/comments | https://api.github.com/repos/huggingface/datasets/issues/1876/events | https://github.com/huggingface/datasets/issues/1876 | 808,025,859 | MDU6SXNzdWU4MDgwMjU4NTk= | 1,876 | load_dataset("multi_woz_v22") NonMatchingChecksumError | {
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"Thanks for reporting !\r\nThis is due to the changes made in the data files in the multiwoz repo: https://github.com/budzianowski/multiwoz/pull/59\r\nI'm opening a PR to update the checksums of the data files.",
"I just merged the fix. It will be available in the new release of `datasets` later today.\r\nYou'll ... | 1,613,330,088,000 | 1,628,100,480,000 | 1,628,100,480,000 | NONE | null | null | null | Hi, it seems that loading the multi_woz_v22 dataset gives a NonMatchingChecksumError.
To reproduce:
`dataset = load_dataset('multi_woz_v22','v2.2_active_only',split='train')`
This will give the following error:
```
raise NonMatchingChecksumError(error_msg + str(bad_urls))
datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:
['https://github.com/budzianowski/multiwoz/raw/master/data/MultiWOZ_2.2/dialog_acts.json', 'https://github.com/budzianowski/multiwoz/raw/master/data/MultiWOZ_2.2/train/dialogues_001.json', 'https://github.com/budzianowski/multiwoz/raw/master/data/MultiWOZ_2.2/train/dialogues_003.json', 'https://github.com/budzianowski/multiwoz/raw/master/data/MultiWOZ_2.2/train/dialogues_004.json', 'https://github.com/budzianowski/multiwoz/raw/master/data/MultiWOZ_2.2/train/dialogues_005.json', 'https://github.com/budzianowski/multiwoz/raw/master/data/MultiWOZ_2.2/train/dialogues_006.json', 'https://github.com/budzianowski/multiwoz/raw/master/data/MultiWOZ_2.2/train/dialogues_007.json', 'https://github.com/budzianowski/multiwoz/raw/master/data/MultiWOZ_2.2/train/dialogues_008.json', 'https://github.com/budzianowski/multiwoz/raw/master/data/MultiWOZ_2.2/train/dialogues_009.json', 'https://github.com/budzianowski/multiwoz/raw/master/data/MultiWOZ_2.2/train/dialogues_010.json', 'https://github.com/budzianowski/multiwoz/raw/master/data/MultiWOZ_2.2/train/dialogues_012.json', 'https://github.com/budzianowski/multiwoz/raw/master/data/MultiWOZ_2.2/train/dialogues_013.json', 'https://github.com/budzianowski/multiwoz/raw/master/data/MultiWOZ_2.2/train/dialogues_014.json', 'https://github.com/budzianowski/multiwoz/raw/master/data/MultiWOZ_2.2/train/dialogues_015.json', 'https://github.com/budzianowski/multiwoz/raw/master/data/MultiWOZ_2.2/train/dialogues_016.json', 'https://github.com/budzianowski/multiwoz/raw/master/data/MultiWOZ_2.2/train/dialogues_017.json', 'https://github.com/budzianowski/multiwoz/raw/master/data/MultiWOZ_2.2/dev/dialogues_001.json', 'https://github.com/budzianowski/multiwoz/raw/master/data/MultiWOZ_2.2/dev/dialogues_002.json', 'https://github.com/budzianowski/multiwoz/raw/master/data/MultiWOZ_2.2/test/dialogues_001.json', 'https://github.com/budzianowski/multiwoz/raw/master/data/MultiWOZ_2.2/test/dialogues_002.json']
```
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"is there a way to check errors without subscribing to CircleCI? Because they want access to private repositories when logging.",
"I think you need to be logged in to check the errors unfortunately. Feel free to create an account with bitbucket maybe if you don't want it to access your private github repos",
"I... | 1,613,235,724,000 | 1,614,854,302,000 | 1,614,854,302,000 | CONTRIBUTOR | null | false | {
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} | Implementation of Europarl bilingual dataset from described [here](https://opus.nlpl.eu/Europarl.php).
This dataset allows to use every language pair detailed in the original dataset. The loading script manages also the small errors contained in the original dataset (in very rare cases (1 over 10M) there are some keys that references to inexistent sentences).
I chose to follow the the style of a similar dataset available in this repository: `multi_para_crawl`.
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https://api.github.com/repos/huggingface/datasets/issues/1873 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1873/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1873/comments | https://api.github.com/repos/huggingface/datasets/issues/1873/events | https://github.com/huggingface/datasets/pull/1873 | 807,750,745 | MDExOlB1bGxSZXF1ZXN0NTcyOTM4MTYy | 1,873 | add iapp_wiki_qa_squad | {
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} | `iapp_wiki_qa_squad` is an extractive question answering dataset from Thai Wikipedia articles.
It is adapted from [the original iapp-wiki-qa-dataset](https://github.com/iapp-technology/iapp-wiki-qa-dataset)
to [SQuAD](https://rajpurkar.github.io/SQuAD-explorer/) format, resulting in
5761/742/739 questions from 1529/191/192 articles. | {
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"Hi ! Indeed if you add a column to a formatted dataset, then the new dataset gets a new formatting in which:\r\n```\r\nnew formatted columns = (all columns - previously unformatted columns)\r\n```\r\nTherefore the new column is going to be formatted using the `torch` formatting.\r\n\r\nIf you want your new column ... | 1,613,207,675,000 | 1,617,112,905,000 | 1,617,112,905,000 | NONE | null | null | null | Hi,
thanks for the nice library. I'm in the process of creating a custom dataset, which has a mix of tensors and lists of strings. I stumbled upon an error and want to know if its a problem on my side.
I load some lists of strings and integers, then call `data.set_format("torch", columns=["some_integer_column1", "some_integer_column2"], output_all_columns=True)`. This converts the integer columns into tensors, but keeps the lists of strings as they are. I then call `map` to add a new column to my dataset, which is a **list of strings**. Once I iterate through my dataset, I get an error that the new column can't be converted into a tensor (which is probably caused by `set_format`).
Below some pseudo code:
```python
def augment_func(sample: Dict) -> Dict:
# do something
return {
"some_integer_column1" : augmented_data["some_integer_column1"], # <-- tensor
"some_integer_column2" : augmented_data["some_integer_column2"], # <-- tensor
"NEW_COLUMN": targets, # <-- list of strings
}
data = datasets.load_dataset(__file__, data_dir="...", split="train")
data.set_format("torch", columns=["some_integer_column1", "some_integer_column2"], output_all_columns=True)
augmented_dataset = data.map(augment_func, batched=False)
for sample in augmented_dataset:
print(sample) # fails
```
and the exception:
```python
Traceback (most recent call last):
File "dataset.py", line 487, in <module>
main()
File "dataset.py", line 471, in main
for sample in augmented_dataset:
File "lib/python3.8/site-packages/datasets/arrow_dataset.py", line 697, in __iter__
yield self._getitem(
File "lib/python3.8/site-packages/datasets/arrow_dataset.py", line 1069, in _getitem
outputs = self._convert_outputs(
File "lib/python3.8/site-packages/datasets/arrow_dataset.py", line 890, in _convert_outputs
v = map_nested(command, v, **map_nested_kwargs)
File "lib/python3.8/site-packages/datasets/utils/py_utils.py", line 225, in map_nested
return function(data_struct)
File "lib/python3.8/site-packages/datasets/arrow_dataset.py", line 850, in command
return [map_nested(command, i, **map_nested_kwargs) for i in x]
File "lib/python3.8/site-packages/datasets/arrow_dataset.py", line 850, in <listcomp>
return [map_nested(command, i, **map_nested_kwargs) for i in x]
File "lib/python3.8/site-packages/datasets/utils/py_utils.py", line 225, in map_nested
return function(data_struct)
File "lib/python3.8/site-packages/datasets/arrow_dataset.py", line 850, in command
return [map_nested(command, i, **map_nested_kwargs) for i in x]
File "lib/python3.8/site-packages/datasets/arrow_dataset.py", line 850, in <listcomp>
return [map_nested(command, i, **map_nested_kwargs) for i in x]
File "lib/python3.8/site-packages/datasets/utils/py_utils.py", line 225, in map_nested
return function(data_struct)
File "lib/python3.8/site-packages/datasets/arrow_dataset.py", line 851, in command
return torch.tensor(x, **format_kwargs)
TypeError: new(): invalid data type 'str'
```
Thanks!
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"Thanks for the changes :)\r\nmerging"
] | 1,613,201,483,000 | 1,615,198,365,000 | 1,615,198,365,000 | CONTRIBUTOR | null | false | {
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"Thanks @lhoestq for your remarks. Yes, I agree there are still many issues to be tackled... This PR is just a starting point, so that we can discuss how Dataset should be generalized.",
"Sure ! I opened an issue #1877 so we can discuss this specific aspect :)",
"I am going to implement this consolidation step ... | 1,613,142,226,000 | 1,619,172,091,000 | 1,619,172,091,000 | MEMBER | null | false | {
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} | Implement `Dataset.add_item`.
Close #1854. | {
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https://api.github.com/repos/huggingface/datasets/issues/1869 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1869/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1869/comments | https://api.github.com/repos/huggingface/datasets/issues/1869/events | https://github.com/huggingface/datasets/pull/1869 | 807,159,835 | MDExOlB1bGxSZXF1ZXN0NTcyNDU0NTMy | 1,869 | Remove outdated commands in favor of huggingface-cli | {
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cc @julien-c | {
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https://api.github.com/repos/huggingface/datasets/issues/1868 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1868/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1868/comments | https://api.github.com/repos/huggingface/datasets/issues/1868/events | https://github.com/huggingface/datasets/pull/1868 | 807,138,159 | MDExOlB1bGxSZXF1ZXN0NTcyNDM2MjA0 | 1,868 | Update oscar sizes | {
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} | This commit https://github.com/huggingface/datasets/commit/837a152e4724adc5308e2c4481908c00a8d93383 removed empty lines from the oscar deduplicated datasets. This PR updates the size of each deduplicated dataset to fix possible `NonMatchingSplitsSizesError` errors. cc @cahya-wirawan | {
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https://api.github.com/repos/huggingface/datasets/issues/1867 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1867/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1867/comments | https://api.github.com/repos/huggingface/datasets/issues/1867/events | https://github.com/huggingface/datasets/issues/1867 | 807,127,181 | MDU6SXNzdWU4MDcxMjcxODE= | 1,867 | ERROR WHEN USING SET_TRANSFORM() | {
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"Hi @alejandrocros it looks like an incompatibility with the current Trainer @sgugger \r\nIndeed currently the Trainer of `transformers` doesn't support a dataset with a transform\r\n\r\nIt looks like it comes from this line: https://github.com/huggingface/transformers/blob/f51188cbe74195c14c5b3e2e8f10c2f435f9751a/... | 1,613,126,311,000 | 1,614,607,464,000 | 1,614,168,043,000 | NONE | null | null | null | Hi, I'm trying to use dataset.set_transform(encode) as @lhoestq told me in this issue: https://github.com/huggingface/datasets/issues/1825#issuecomment-774202797
However, when I try to use Trainer from transformers with such dataset, it throws an error:
```
TypeError: __init__() missing 1 required positional argument: 'transform'
[INFO|trainer.py:357] 2021-02-12 10:18:09,893 >> The following columns in the training set don't have a corresponding argument in `AlbertForMaskedLM.forward` and have been ignored: text.
Exception in device=TPU:0: __init__() missing 1 required positional argument: 'transform'
Traceback (most recent call last):
File "/anaconda3/envs/torch-xla-1.7/lib/python3.6/site-packages/torch_xla/distributed/xla_multiprocessing.py", line 330, in _mp_start_fn
_start_fn(index, pf_cfg, fn, args)
File "/anaconda3/envs/torch-xla-1.7/lib/python3.6/site-packages/torch_xla/distributed/xla_multiprocessing.py", line 324, in _start_fn
fn(gindex, *args)
File "/home/alejandro_vaca/transformers/examples/language-modeling/run_mlm_wwm.py", line 368, in _mp_fn
main()
File "/home/alejandro_vaca/transformers/examples/language-modeling/run_mlm_wwm.py", line 332, in main
data_collator=data_collator,
File "/anaconda3/envs/torch-xla-1.7/lib/python3.6/site-packages/transformers/trainer.py", line 286, in __init__
self._remove_unused_columns(self.train_dataset, description="training")
File "/anaconda3/envs/torch-xla-1.7/lib/python3.6/site-packages/transformers/trainer.py", line 359, in _remove_unused_columns
dataset.set_format(type=dataset.format["type"], columns=columns)
File "/home/alejandro_vaca/datasets/src/datasets/fingerprint.py", line 312, in wrapper
out = func(self, *args, **kwargs)
File "/home/alejandro_vaca/datasets/src/datasets/arrow_dataset.py", line 818, in set_format
_ = get_formatter(type, **format_kwargs)
File "/home/alejandro_vaca/datasets/src/datasets/formatting/__init__.py", line 112, in get_formatter
return _FORMAT_TYPES[format_type](**format_kwargs)
TypeError: __init__() missing 1 required positional argument: 'transform'
```
The code I'm using:
```{python}
def tokenize_function(examples):
# Remove empty lines
examples["text"] = [line for line in examples["text"] if len(line) > 0 and not line.isspace()]
return tokenizer(examples["text"], padding=padding, truncation=True, max_length=data_args.max_seq_length)
datasets.set_transform(tokenize_function)
data_collator = DataCollatorForWholeWordMask(tokenizer=tokenizer, mlm_probability=data_args.mlm_probability)
# Initialize our Trainer
trainer = Trainer(
model=model,
args=training_args,
train_dataset=datasets["train"] if training_args.do_train else None,
eval_dataset=datasets["val"] if training_args.do_eval else None,
tokenizer=tokenizer,
data_collator=data_collator,
)
```
I've installed from source, master branch.
| {
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https://api.github.com/repos/huggingface/datasets/issues/1866 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1866/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1866/comments | https://api.github.com/repos/huggingface/datasets/issues/1866/events | https://github.com/huggingface/datasets/pull/1866 | 807,017,816 | MDExOlB1bGxSZXF1ZXN0NTcyMzM3NDQ1 | 1,866 | Add dataset for Financial PhraseBank | {
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"Thanks for the feedback. All accepted and metadata regenerated."
] | 1,613,115,056,000 | 1,613,571,756,000 | 1,613,571,756,000 | CONTRIBUTOR | null | false | {
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https://api.github.com/repos/huggingface/datasets/issues/1865 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1865/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1865/comments | https://api.github.com/repos/huggingface/datasets/issues/1865/events | https://github.com/huggingface/datasets/pull/1865 | 806,388,290 | MDExOlB1bGxSZXF1ZXN0NTcxODE2ODI2 | 1,865 | Updated OPUS Open Subtitles Dataset with metadata information | {
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"Hi !\r\nAbout the problems you mentioned:\r\n- Saving the infos is only done for the configurations inside the BUILDER_CONFIGS. Otherwise you would need to run the scripts on ALL language pairs, which is not what we want.\r\n- Moreover when you're on your branch, please specify the path to your local version of th... | 1,613,049,986,000 | 1,613,738,289,000 | 1,613,149,184,000 | CONTRIBUTOR | null | false | {
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} | Close #1844
Problems:
- I ran `python datasets-cli test datasets/open_subtitles --save_infos --all_configs`, hence the change in `dataset_infos.json`, but it appears that the metadata features have not been added for all pairs. Any idea why that might be?
- Possibly related to the above, I tried doing `pip uninstall datasets && pip install -e ".[dev]"` after the changes, and loading the dataset via `load_dataset("open_subtitles", lang1='hi', lang2='it')` to check if the update worked, but the loaded dataset did not contain the metadata fields (neither in the features nor doing `next(iter(dataset['train']))`). What step(s) did I miss?
Questions:
- Is it ok to have a `classmethod` in there? I have not seen any in the few other datasets I have checked. I could make it a local method of the `_generate_examples` method, but I'd rather not duplicate the logic... | {
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https://api.github.com/repos/huggingface/datasets/issues/1864 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1864/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1864/comments | https://api.github.com/repos/huggingface/datasets/issues/1864/events | https://github.com/huggingface/datasets/issues/1864 | 806,172,843 | MDU6SXNzdWU4MDYxNzI4NDM= | 1,864 | Add Winogender Schemas | {
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"Nevermind, this one is already available on the hub under the name `'wino_bias'`: https://huggingface.co/datasets/wino_bias"
] | 1,613,031,518,000 | 1,613,031,591,000 | 1,613,031,591,000 | NONE | null | null | null | ## Adding a Dataset
- **Name:** Winogender Schemas
- **Description:** Winogender Schemas (inspired by Winograd Schemas) are minimal pairs of sentences that differ only by the gender of one pronoun in the sentence, designed to test for the presence of gender bias in automated coreference resolution systems.
- **Paper:** https://arxiv.org/abs/1804.09301
- **Data:** https://github.com/rudinger/winogender-schemas (see data directory)
- **Motivation:** Testing gender bias in automated coreference resolution systems, improve coreference resolution in general.
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
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https://api.github.com/repos/huggingface/datasets/issues/1863 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1863/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1863/comments | https://api.github.com/repos/huggingface/datasets/issues/1863/events | https://github.com/huggingface/datasets/issues/1863 | 806,171,311 | MDU6SXNzdWU4MDYxNzEzMTE= | 1,863 | Add WikiCREM | {
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"Hi @NielsRogge I would like to work on this dataset.\r\n\r\nThanks!",
"Hi @udapy, are you working on this?"
] | 1,613,031,360,000 | 1,615,102,033,000 | null | NONE | null | null | null | ## Adding a Dataset
- **Name:** WikiCREM
- **Description:** A large unsupervised corpus for coreference resolution.
- **Paper:** https://arxiv.org/abs/1905.06290
- **Github repo:**: https://github.com/vid-koci/bert-commonsense
- **Data:** https://ora.ox.ac.uk/objects/uuid:c83e94bb-7584-41a1-aef9-85b0e764d9e3
- **Motivation:** Coreference resolution, common sense reasoning
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
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https://api.github.com/repos/huggingface/datasets/issues/1862 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1862/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1862/comments | https://api.github.com/repos/huggingface/datasets/issues/1862/events | https://github.com/huggingface/datasets/pull/1862 | 805,722,293 | MDExOlB1bGxSZXF1ZXN0NTcxMjc2ODAx | 1,862 | Fix writing GPU Faiss index | {
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} | As reported in by @corticalstack there is currently an error when we try to save a faiss index on GPU.
I fixed that by checking the index `getDevice()` method before calling `index_gpu_to_cpu`
Close #1859 | {
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https://api.github.com/repos/huggingface/datasets/issues/1861 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1861/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1861/comments | https://api.github.com/repos/huggingface/datasets/issues/1861/events | https://github.com/huggingface/datasets/pull/1861 | 805,631,215 | MDExOlB1bGxSZXF1ZXN0NTcxMjAwNjA1 | 1,861 | Fix Limit url | {
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} | The test.json file of the Literal-Motion-in-Text (LiMiT) dataset was removed recently on the master branch of the repo at https://github.com/ilmgut/limit_dataset
This PR uses the previous commit sha to download the file instead, as suggested by @Paethon
Close #1836 | {
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https://api.github.com/repos/huggingface/datasets/issues/1860 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1860/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1860/comments | https://api.github.com/repos/huggingface/datasets/issues/1860/events | https://github.com/huggingface/datasets/pull/1860 | 805,510,037 | MDExOlB1bGxSZXF1ZXN0NTcxMDk4OTIz | 1,860 | Add loading from the Datasets Hub + add relative paths in download manager | {
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"I just added the steps to share a dataset on the datasets hub. It's highly inspired by the steps to share a model in the `transformers` doc.\r\n\r\nMoreover once the new huggingface_hub is released we can update the version in the setup.py. We also need to update the command to create a dataset repo in the documen... | 1,612,963,451,000 | 1,613,157,210,000 | 1,613,157,209,000 | MEMBER | null | false | {
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} | With the new Datasets Hub on huggingface.co it's now possible to have a dataset repo with your own script and data.
For example: https://huggingface.co/datasets/lhoestq/custom_squad/tree/main contains one script and two json files.
You can load it using
```python
from datasets import load_dataset
d = load_dataset("lhoestq/custom_squad")
```
To be able to use the data files that live right next to the dataset script on the repo in the hub, I added relative paths support for the DownloadManager. For example in the repo mentioned above, there are two json files that can be downloaded via
```python
_URLS = {
"train": "train-v1.1.json",
"dev": "dev-v1.1.json",
}
downloaded_files = dl_manager.download_and_extract(_URLS)
```
To make it work, I set the `base_path` of the DownloadManager to be the parent path of the dataset script (which comes from either a local path or a remote url).
I also had to add the auth header of the requests to huggingface.co for private datasets repos. The token is fetched from [huggingface_hub](https://github.com/huggingface/huggingface_hub). | {
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https://api.github.com/repos/huggingface/datasets/issues/1859 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1859/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1859/comments | https://api.github.com/repos/huggingface/datasets/issues/1859/events | https://github.com/huggingface/datasets/issues/1859 | 805,479,025 | MDU6SXNzdWU4MDU0NzkwMjU= | 1,859 | Error "in void don't know how to serialize this type of index" when saving index to disk when device=0 (GPU) | {
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"Hi @corticalstack ! Thanks for reporting. Indeed in the recent versions of Faiss we must use `getDevice` to check if the index in on GPU.\r\n\r\nI'm opening a PR",
"I fixed this issue. It should work fine now.\r\nFeel free to try it out by installing `datasets` from source.\r\nOtherwise you can wait for the next... | 1,612,960,860,000 | 1,612,981,932,000 | 1,612,981,067,000 | NONE | null | null | null | Error serializing faiss index. Error as follows:
`Error in void faiss::write_index(const faiss::Index*, faiss::IOWriter*) at /home/conda/feedstock_root/build_artifacts/faiss-split_1612472484670/work/faiss/impl/index_write.cpp:453: don't know how to serialize this type of index`
Note:
`torch.cuda.is_available()` reports:
```
Cuda is available
cuda:0
```
Adding index, device=0 for GPU.
`dataset.add_faiss_index(column='embeddings', index_name='idx_embeddings', device=0)`
However, during a quick debug, self.faiss_index has no attr "device" when checked in` search.py, method save`, so fails to transform gpu index to cpu index. If I add index without device, index is saved OK.
```
def save(self, file: str):
"""Serialize the FaissIndex on disk"""
import faiss # noqa: F811
if (
hasattr(self.faiss_index, "device")
and self.faiss_index.device is not None
and self.faiss_index.device > -1
):
index = faiss.index_gpu_to_cpu(self.faiss_index)
else:
index = self.faiss_index
faiss.write_index(index, file)
```
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https://api.github.com/repos/huggingface/datasets/issues/1858 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1858/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1858/comments | https://api.github.com/repos/huggingface/datasets/issues/1858/events | https://github.com/huggingface/datasets/pull/1858 | 805,477,774 | MDExOlB1bGxSZXF1ZXN0NTcxMDcxNzIx | 1,858 | Clean config getenvs | {
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} | Following #1848
Remove double getenv calls and fix one issue with rarfile
cc @albertvillanova | {
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https://api.github.com/repos/huggingface/datasets/issues/1857 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1857/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1857/comments | https://api.github.com/repos/huggingface/datasets/issues/1857/events | https://github.com/huggingface/datasets/issues/1857 | 805,391,107 | MDU6SXNzdWU4MDUzOTExMDc= | 1,857 | Unable to upload "community provided" dataset - 400 Client Error | {
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"Hi ! We're in the process of switching the community datasets to git repos, exactly like what we're doing for models.\r\nYou can find an example here:\r\nhttps://huggingface.co/datasets/lhoestq/custom_squad/tree/main\r\n\r\nWe'll update the CLI in the coming days and do a new release :)\r\n\r\nAlso cc @julien-c ma... | 1,612,953,541,000 | 1,627,967,173,000 | 1,627,967,173,000 | CONTRIBUTOR | null | null | null | Hi,
i'm trying to a upload a dataset as described [here](https://huggingface.co/docs/datasets/v1.2.0/share_dataset.html#sharing-a-community-provided-dataset). This is what happens:
```
$ datasets-cli login
$ datasets-cli upload_dataset my_dataset
About to upload file /path/to/my_dataset/dataset_infos.json to S3 under filename my_dataset/dataset_infos.json and namespace username
About to upload file /path/to/my_dataset/my_dataset.py to S3 under filename my_dataset/my_dataset.py and namespace username
Proceed? [Y/n] Y
Uploading... This might take a while if files are large
400 Client Error: Bad Request for url: https://huggingface.co/api/datasets/presign
huggingface.co migrated to a new model hosting system.
You need to upgrade to transformers v3.5+ to upload new models.
More info at https://discuss.hugginface.co or https://twitter.com/julien_c. Thank you!
```
I'm using the latest releases of datasets and transformers. | {
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https://api.github.com/repos/huggingface/datasets/issues/1856 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1856/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1856/comments | https://api.github.com/repos/huggingface/datasets/issues/1856/events | https://github.com/huggingface/datasets/issues/1856 | 805,360,200 | MDU6SXNzdWU4MDUzNjAyMDA= | 1,856 | load_dataset("amazon_polarity") NonMatchingChecksumError | {
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"Hi ! This issue may be related to #996 \r\nThis comes probably from the Quota Exceeded error from Google Drive.\r\nCan you try again tomorrow and see if you still have the error ?\r\n\r\nOn my side I didn't get any error today with `load_dataset(\"amazon_polarity\")`",
"+1 encountering this issue as well",
"@l... | 1,612,951,256,000 | 1,626,872,391,000 | null | NONE | null | null | null | Hi, it seems that loading the amazon_polarity dataset gives a NonMatchingChecksumError.
To reproduce:
```
load_dataset("amazon_polarity")
```
This will give the following error:
```
---------------------------------------------------------------------------
NonMatchingChecksumError Traceback (most recent call last)
<ipython-input-3-8559a03fe0f8> in <module>()
----> 1 dataset = load_dataset("amazon_polarity")
3 frames
/usr/local/lib/python3.6/dist-packages/datasets/utils/info_utils.py in verify_checksums(expected_checksums, recorded_checksums, verification_name)
37 if len(bad_urls) > 0:
38 error_msg = "Checksums didn't match" + for_verification_name + ":\n"
---> 39 raise NonMatchingChecksumError(error_msg + str(bad_urls))
40 logger.info("All the checksums matched successfully" + for_verification_name)
41
NonMatchingChecksumError: Checksums didn't match for dataset source files:
['https://drive.google.com/u/0/uc?id=0Bz8a_Dbh9QhbaW12WVVZS2drcnM&export=download']
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/1854 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1854/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1854/comments | https://api.github.com/repos/huggingface/datasets/issues/1854/events | https://github.com/huggingface/datasets/issues/1854 | 805,204,397 | MDU6SXNzdWU4MDUyMDQzOTc= | 1,854 | Feature Request: Dataset.add_item | {
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"Hi @sshleifer.\r\n\r\nI am not sure of understanding the need of the `add_item` approach...\r\n\r\nBy just reading your \"Desired API\" section, I would say you could (nearly) get it with a 1-column Dataset:\r\n```python\r\ndata = {\"input_ids\": [np.array([4,4,2]), np.array([8,6,5,5,2]), np.array([3,3,31,5])]}\r\... | 1,612,937,160,000 | 1,619,172,090,000 | 1,619,172,090,000 | CONTRIBUTOR | null | null | null | I'm trying to integrate `huggingface/datasets` functionality into `fairseq`, which requires (afaict) being able to build a dataset through an `add_item` method, such as https://github.com/pytorch/fairseq/blob/master/fairseq/data/indexed_dataset.py#L318, as opposed to loading all the text into arrow, and then `dataset.map(binarizer)`.
Is this possible at the moment? Is there an example? I'm happy to use raw `pa.Table` but not sure whether it will support uneven length entries.
### Desired API
```python
import numpy as np
tokenized: List[np.NDArray[np.int64]] = [np.array([4,4,2]), np.array([8,6,5,5,2]), np.array([3,3,31,5])
def build_dataset_from_tokenized(tokenized: List[np.NDArray[int]]) -> Dataset:
"""FIXME"""
dataset = EmptyDataset()
for t in tokenized: dataset.append(t)
return dataset
ds = build_dataset_from_tokenized(tokenized)
assert (ds[0] == np.array([4,4,2])).all()
```
### What I tried
grep, google for "add one entry at a time", "datasets.append"
### Current Code
This code achieves the same result but doesn't fit into the `add_item` abstraction.
```python
dataset = load_dataset('text', data_files={'train': 'train.txt'})
tokenizer = RobertaTokenizerFast.from_pretrained('roberta-base', max_length=4096)
def tokenize_function(examples):
ids = tokenizer(examples['text'], return_attention_mask=False)['input_ids']
return {'input_ids': [x[1:] for x in ids]}
ds = dataset.map(tokenize_function, batched=True, num_proc=4, remove_columns=['text'], load_from_cache_file=not overwrite_cache)
print(ds['train'][0]) => np array
```
Thanks in advance! | {
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} | Configure library root logger at the datasets.logging module level (singleton-like).
By doing it this way:
- we are sure configuration is done only once: module level code is only runned once
- no need of global variable
- no need of threading lock | {
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https://api.github.com/repos/huggingface/datasets/issues/1852 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1852/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1852/comments | https://api.github.com/repos/huggingface/datasets/issues/1852/events | https://github.com/huggingface/datasets/pull/1852 | 804,633,033 | MDExOlB1bGxSZXF1ZXN0NTcwMzY3NTU1 | 1,852 | Add Arabic Speech Corpus | {
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https://api.github.com/repos/huggingface/datasets/issues/1851 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1851/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1851/comments | https://api.github.com/repos/huggingface/datasets/issues/1851/events | https://github.com/huggingface/datasets/pull/1851 | 804,523,174 | MDExOlB1bGxSZXF1ZXN0NTcwMjc2MTk5 | 1,851 | set bert_score version dependency | {
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https://api.github.com/repos/huggingface/datasets/issues/1850 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1850/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1850/comments | https://api.github.com/repos/huggingface/datasets/issues/1850/events | https://github.com/huggingface/datasets/pull/1850 | 804,412,249 | MDExOlB1bGxSZXF1ZXN0NTcwMTg0MDAx | 1,850 | Add cord 19 dataset | {
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"Cleaned-up version of previous PR: https://github.com/huggingface/datasets/pull/1129",
"@lhoestq FYI",
"Before merging I might tweak a little bit the dummy data to avoid having to check if the `document_parses` and `embeddings` directories exist or not. I'll do that later today",
"Looks all good now ! Thanks... | 1,612,866,128,000 | 1,612,883,786,000 | 1,612,883,786,000 | CONTRIBUTOR | null | false | {
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} | Initial version only reading the metadata in CSV.
### Checklist:
- [x] Create the dataset script /datasets/my_dataset/my_dataset.py using the template
- [x] Fill the _DESCRIPTION and _CITATION variables
- [x] Implement _infos(), _split_generators() and _generate_examples()
- [x] Make sure that the BUILDER_CONFIGS class attribute is filled with the different configurations of the dataset and that the BUILDER_CONFIG_CLASS is specified if there is a custom config class.
- [x] Generate the metadata file dataset_infos.json for all configurations
- [x] Generate the dummy data dummy_data.zip files to have the dataset script tested and that they don't weigh too much (<50KB)
- [x] Add the dataset card README.md using the template and at least fill the tags
- [x] Both tests for the real data and the dummy data pass.
### Extras:
- [x] add more metadata
- [x] add full text
- [x] add pre-computed document embedding | {
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