| | --- |
| | language: en |
| | license: cc-by-4.0 |
| | datasets: |
| | - squad_v2 |
| | model-index: |
| | - name: deepset/roberta-base-squad2 |
| | results: |
| | - task: |
| | type: question-answering |
| | name: Question Answering |
| | dataset: |
| | name: squad_v2 |
| | type: squad_v2 |
| | config: squad_v2 |
| | split: validation |
| | metrics: |
| | - type: exact_match |
| | value: 79.9309 |
| | name: Exact Match |
| | verified: true |
| | verifyToken: >- |
| | eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMDhhNjg5YzNiZGQ1YTIyYTAwZGUwOWEzZTRiYzdjM2QzYjA3ZTUxNDM1NjE1MTUyMjE1MGY1YzEzMjRjYzVjYiIsInZlcnNpb24iOjF9.EH5JJo8EEFwU7osPz3s7qanw_tigeCFhCXjSfyN0Y1nWVnSfulSxIk_DbAEI5iE80V4EKLyp5-mYFodWvL2KDA |
| | - type: f1 |
| | value: 82.9501 |
| | name: F1 |
| | verified: true |
| | verifyToken: >- |
| | eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMjk5ZDYwOGQyNjNkMWI0OTE4YzRmOTlkY2JjNjQ0YTZkNTMzMzNkYTA0MDFmNmI3NjA3NjNlMjhiMDQ2ZjJjNSIsInZlcnNpb24iOjF9.DDm0LNTkdLbGsue58bg1aH_s67KfbcmkvL-6ZiI2s8IoxhHJMSf29H_uV2YLyevwx900t-MwTVOW3qfFnMMEAQ |
| | - type: total |
| | value: 11869 |
| | name: total |
| | verified: true |
| | verifyToken: >- |
| | eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMGFkMmI2ODM0NmY5NGNkNmUxYWViOWYxZDNkY2EzYWFmOWI4N2VhYzY5MGEzMTVhOTU4Zjc4YWViOGNjOWJjMCIsInZlcnNpb24iOjF9.fexrU1icJK5_MiifBtZWkeUvpmFISqBLDXSQJ8E6UnrRof-7cU0s4tX_dIsauHWtUpIHMPZCf5dlMWQKXZuAAA |
| | - task: |
| | type: question-answering |
| | name: Question Answering |
| | dataset: |
| | name: squad |
| | type: squad |
| | config: plain_text |
| | split: validation |
| | metrics: |
| | - type: exact_match |
| | value: 85.289 |
| | name: Exact Match |
| | - type: f1 |
| | value: 91.841 |
| | name: F1 |
| | - task: |
| | type: question-answering |
| | name: Question Answering |
| | dataset: |
| | name: adversarial_qa |
| | type: adversarial_qa |
| | config: adversarialQA |
| | split: validation |
| | metrics: |
| | - type: exact_match |
| | value: 29.5 |
| | name: Exact Match |
| | - type: f1 |
| | value: 40.367 |
| | name: F1 |
| | - task: |
| | type: question-answering |
| | name: Question Answering |
| | dataset: |
| | name: squad_adversarial |
| | type: squad_adversarial |
| | config: AddOneSent |
| | split: validation |
| | metrics: |
| | - type: exact_match |
| | value: 78.567 |
| | name: Exact Match |
| | - type: f1 |
| | value: 84.469 |
| | name: F1 |
| | - task: |
| | type: question-answering |
| | name: Question Answering |
| | dataset: |
| | name: squadshifts amazon |
| | type: squadshifts |
| | config: amazon |
| | split: test |
| | metrics: |
| | - type: exact_match |
| | value: 69.924 |
| | name: Exact Match |
| | - type: f1 |
| | value: 83.284 |
| | name: F1 |
| | - task: |
| | type: question-answering |
| | name: Question Answering |
| | dataset: |
| | name: squadshifts new_wiki |
| | type: squadshifts |
| | config: new_wiki |
| | split: test |
| | metrics: |
| | - type: exact_match |
| | value: 81.204 |
| | name: Exact Match |
| | - type: f1 |
| | value: 90.595 |
| | name: F1 |
| | - task: |
| | type: question-answering |
| | name: Question Answering |
| | dataset: |
| | name: squadshifts nyt |
| | type: squadshifts |
| | config: nyt |
| | split: test |
| | metrics: |
| | - type: exact_match |
| | value: 82.931 |
| | name: Exact Match |
| | - type: f1 |
| | value: 90.756 |
| | name: F1 |
| | - task: |
| | type: question-answering |
| | name: Question Answering |
| | dataset: |
| | name: squadshifts reddit |
| | type: squadshifts |
| | config: reddit |
| | split: test |
| | metrics: |
| | - type: exact_match |
| | value: 71.55 |
| | name: Exact Match |
| | - type: f1 |
| | value: 82.939 |
| | name: F1 |
| | base_model: |
| | - FacebookAI/roberta-base |
| | --- |
| | |
| | # roberta-base for Extractive QA |
| |
|
| | This is the [roberta-base](https://huggingface.co/roberta-base) model, fine-tuned using the [SQuAD2.0](https://huggingface.co/datasets/squad_v2) dataset. It's been trained on question-answer pairs, including unanswerable questions, for the task of Extractive Question Answering. |
| | We have also released a distilled version of this model called [deepset/tinyroberta-squad2](https://huggingface.co/deepset/tinyroberta-squad2). It has a comparable prediction quality and runs at twice the speed of [deepset/roberta-base-squad2](https://huggingface.co/deepset/roberta-base-squad2). |
| |
|
| |
|
| | ## Overview |
| | **Language model:** roberta-base |
| | **Language:** English |
| | **Downstream-task:** Extractive QA |
| | **Training data:** SQuAD 2.0 |
| | **Eval data:** SQuAD 2.0 |
| | **Code:** See [an example extractive QA pipeline built with Haystack](https://haystack.deepset.ai/tutorials/34_extractive_qa_pipeline) |
| | **Infrastructure**: 4x Tesla v100 |
| |
|
| | ## Hyperparameters |
| |
|
| | ``` |
| | batch_size = 96 |
| | n_epochs = 2 |
| | base_LM_model = "roberta-base" |
| | max_seq_len = 386 |
| | learning_rate = 3e-5 |
| | lr_schedule = LinearWarmup |
| | warmup_proportion = 0.2 |
| | doc_stride=128 |
| | max_query_length=64 |
| | ``` |
| |
|
| | ## Usage |
| |
|
| | ### In Haystack |
| | Haystack is an AI orchestration framework to build customizable, production-ready LLM applications. You can use this model in Haystack to do extractive question answering on documents. |
| | To load and run the model with [Haystack](https://github.com/deepset-ai/haystack/): |
| | ```python |
| | # After running pip install haystack-ai "transformers[torch,sentencepiece]" |
| | |
| | from haystack import Document |
| | from haystack.components.readers import ExtractiveReader |
| | |
| | docs = [ |
| | Document(content="Python is a popular programming language"), |
| | Document(content="python ist eine beliebte Programmiersprache"), |
| | ] |
| | |
| | reader = ExtractiveReader(model="deepset/roberta-base-squad2") |
| | reader.warm_up() |
| | |
| | question = "What is a popular programming language?" |
| | result = reader.run(query=question, documents=docs) |
| | # {'answers': [ExtractedAnswer(query='What is a popular programming language?', score=0.5740374326705933, data='python', document=Document(id=..., content: '...'), context=None, document_offset=ExtractedAnswer.Span(start=0, end=6),...)]} |
| | ``` |
| | For a complete example with an extractive question answering pipeline that scales over many documents, check out the [corresponding Haystack tutorial](https://haystack.deepset.ai/tutorials/34_extractive_qa_pipeline). |
| |
|
| | ### In Transformers |
| | ```python |
| | from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline |
| | |
| | model_name = "deepset/roberta-base-squad2" |
| | |
| | # a) Get predictions |
| | nlp = pipeline('question-answering', model=model_name, tokenizer=model_name) |
| | QA_input = { |
| | 'question': 'Why is model conversion important?', |
| | 'context': 'The option to convert models between FARM and transformers gives freedom to the user and let people easily switch between frameworks.' |
| | } |
| | res = nlp(QA_input) |
| | |
| | # b) Load model & tokenizer |
| | model = AutoModelForQuestionAnswering.from_pretrained(model_name) |
| | tokenizer = AutoTokenizer.from_pretrained(model_name) |
| | ``` |
| |
|
| | ## Performance |
| | Evaluated on the SQuAD 2.0 dev set with the [official eval script](https://worksheets.codalab.org/rest/bundles/0x6b567e1cf2e041ec80d7098f031c5c9e/contents/blob/). |
| |
|
| | ``` |
| | "exact": 79.87029394424324, |
| | "f1": 82.91251169582613, |
| | |
| | "total": 11873, |
| | "HasAns_exact": 77.93522267206478, |
| | "HasAns_f1": 84.02838248389763, |
| | "HasAns_total": 5928, |
| | "NoAns_exact": 81.79983179142137, |
| | "NoAns_f1": 81.79983179142137, |
| | "NoAns_total": 5945 |
| | ``` |
| |
|
| | ## Authors |
| | **Branden Chan:** branden.chan@deepset.ai |
| | **Timo M枚ller:** timo.moeller@deepset.ai |
| | **Malte Pietsch:** malte.pietsch@deepset.ai |
| | **Tanay Soni:** tanay.soni@deepset.ai |
| |
|
| | ## About us |
| |
|
| | <div class="grid lg:grid-cols-2 gap-x-4 gap-y-3"> |
| | <div class="w-full h-40 object-cover mb-2 rounded-lg flex items-center justify-center"> |
| | <img alt="" src="https://raw.githubusercontent.com/deepset-ai/.github/main/deepset-logo-colored.png" class="w-40"/> |
| | </div> |
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| | <img alt="" src="https://raw.githubusercontent.com/deepset-ai/.github/main/haystack-logo-colored.png" class="w-40"/> |
| | </div> |
| | </div> |
| | |
| | [deepset](http://deepset.ai/) is the company behind the production-ready open-source AI framework [Haystack](https://haystack.deepset.ai/). |
| |
|
| | Some of our other work: |
| | - [Distilled roberta-base-squad2 (aka "tinyroberta-squad2")](https://huggingface.co/deepset/tinyroberta-squad2) |
| | - [German BERT](https://deepset.ai/german-bert), [GermanQuAD and GermanDPR](https://deepset.ai/germanquad), [German embedding model](https://huggingface.co/mixedbread-ai/deepset-mxbai-embed-de-large-v1) |
| | - [deepset Cloud](https://www.deepset.ai/deepset-cloud-product) |
| | - [deepset Studio](https://www.deepset.ai/deepset-studio) |
| |
|
| | ## Get in touch and join the Haystack community |
| |
|
| | <p>For more info on Haystack, visit our <strong><a href="https://github.com/deepset-ai/haystack">GitHub</a></strong> repo and <strong><a href="https://docs.haystack.deepset.ai">Documentation</a></strong>. |
| |
|
| | We also have a <strong><a class="h-7" href="https://haystack.deepset.ai/community">Discord community open to everyone!</a></strong></p> |
| |
|
| | [Twitter](https://twitter.com/Haystack_AI) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Discord](https://haystack.deepset.ai/community) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://haystack.deepset.ai/) | [YouTube](https://www.youtube.com/@deepset_ai) |
| |
|
| | By the way: [we're hiring!](http://www.deepset.ai/jobs) |
