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README.md

@@ -1,199 +1,176 @@
 ---
+language:
+- en
+license: apache-2.0
 library_name: transformers
-tags: []
+pipeline_tag: text2text-generation
+base_model: google-t5/t5-base
+datasets:
+- sentence-transformers/codesearchnet
+tags:
+- t5
+- code-summarization
+- python
+- code
+metrics:
+- bleu
+- rouge
+model-index:
+- name: t5-base-code-summarization
+  results:
+  - task:
+      type: summarization
+      name: Code Summarization
+    dataset:
+      name: CodeSearchNet (Python, held-out split)
+      type: sentence-transformers/codesearchnet
+    metrics:
+      - type: bleu
+        name: BLEU
+        value: 3.92
+      - type: bleu
+        name: Smoothed BLEU-4
+        value: 6.65
+      - type: rouge
+        name: ROUGE-1
+        value: 36.03
+      - type: rouge
+        name: ROUGE-2
+        value: 12.61
+      - type: rouge
+        name: ROUGE-L
+        value: 32.97
 ---
 
-# Model Card for Model ID
+# t5-base-code-summarization
 
-<!-- Provide a quick summary of what the model is/does. -->
+[`google-t5/t5-base`](https://huggingface.co/google-t5/t5-base) (223M parameters) fine-tuned to
+generate a one-sentence natural-language summary (docstring) for a **Python function**.
 
+- **Input:** `"summarize code: " + <python source code>` (the prefix is required)
+- **Output:** a short English summary of what the function does
+- **Language:** Python only
 
+## Usage
 
-## Model Details
+```python
+from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
 
-### Model Description
+repo = "thealper2/t5-base-code-summarization"
+tokenizer = AutoTokenizer.from_pretrained(repo)
+model = AutoModelForSeq2SeqLM.from_pretrained(repo)
 
-<!-- Provide a longer summary of what this model is. -->
+code = """def calculate_average(numbers):
+    return sum(numbers) / len(numbers)"""
 
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-
-### Model Sources [optional]
-
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-
-## Uses
-
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
-### Direct Use
-
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
-
-### Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
-
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-[More Information Needed]
-
-## Training Details
-
-### Training Data
-
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
-[More Information Needed]
-
-### Training Procedure
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
+inputs = tokenizer("summarize code: " + code, return_tensors="pt",
+                   truncation=True, max_length=512)
+# Decoding settings (beam search etc.) are loaded from generation_config.json.
+output = model.generate(**inputs)
+print(tokenizer.decode(output[0], skip_special_tokens=True))
+```
 
 ## Evaluation
 
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
-#### Software
-
-[More Information Needed]
-
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
-
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
-
-## Model Card Authors [optional]
-
-[More Information Needed]
-
-## Model Card Contact
-
-[More Information Needed]
+Scores on 5,000 held-out test functions, never seen during training or model
+selection. Validation scores are from the in-training evaluation subset.
+
+| Metric | Test | Validation |
+|---|---|---|
+| BLEU (sacreBLEU, corpus) | 3.92 | 4.69 |
+| Smoothed BLEU-4 (sentence avg.) | 6.65 | 7.21 |
+| ROUGE-1 | 36.03 | 37.42 |
+| ROUGE-2 | 12.61 | 14.14 |
+| ROUGE-L | 32.97 | 34.02 |
+| Semantic similarity (MiniLM cosine) | 54.02 | – |
+| Avg. generated length (words) | 6.17 | 5.92 |
+| Avg. reference length (words) | 10.02 | 9.90 |
+
+BLEU/ROUGE reward lexical overlap with a single reference docstring, so a correct
+summary phrased differently scores low. Read them alongside the examples below.
+CodeBLEU is not reported: it scores generated *code*, while this model generates English.
+
+## Examples from the test split
+
+```python
+def validate_flavor_data(self, expected, actual):
+
+        self.log.debug('Validating flavor data...')
+        self.log.debug('actual: {}'.format(repr(actual)))
+        act = [a.name for a in actual]
+        return self._validate_list_data(expected, act)
+```
+
+- **Reference:** Validate flavor data.
+- **Generated:** Validate flavor data.
+
+```python
+def check(text):
+    err = "hedging.misc"
+    msg = "Hedging. Just say it."
+
+    narcissism = [
+        "I would argue that",
+        ", so to speak",
+        "to a certain degree",
+    ]
+
+    return existence_check(text, narcissism, err, msg)
+```
+
+- **Reference:** Suggest the preferred forms.
+- **Generated:** Check if hedging is valid.
+
+```python
+def on_source_directory_chooser_clicked(self):
+
+        title = self.tr('Set the source directory for script and scenario')
+        self.choose_directory(self.source_directory, title)
+```
+
+- **Reference:** Autoconnect slot activated when tbSourceDir is clicked.
+- **Generated:** Sets the source directory for script and scenario.
+
+## Training data
+
+[`sentence-transformers/codesearchnet`](https://huggingface.co/datasets/sentence-transformers/codesearchnet) (`pair` config),
+`code` → `comment` pairs. The dataset mixes about six languages without a label, so
+Python functions were detected by parsing with `ast`. Leading docstrings were stripped
+from the code (otherwise the target leaks into the input), summaries were cut to their
+leading prose, and broken, non-English and boilerplate rows were dropped.
+
+| Split | Examples |
+|---|---|
+| train | 20,000 |
+| validation | 5,000 |
+| test | 5,000 |
+
+## Training procedure
+
+| Hyper-parameter | Value |
+|---|---|
+| Learning rate | 0.0003 |
+| Scheduler / warmup | linear / 0.03 |
+| Optimizer | adamw_torch |
+| Effective batch size | 32 (per-device 8 × accumulation 4) |
+| Epochs | 3.0 |
+| Weight decay | 0.01 |
+| Max source / target length | 512 / 64 tokens |
+| Precision | bf16 |
+| Gradient checkpointing | True |
+| Seed | 42 |
+| Training time | 50.49 min |
+| Peak GPU memory | 4.17 GB |
+
+Best checkpoint selected on validation ROUGE-L with early stopping.
+
+### Generation
+
+`num_beams=4`, `max_length=64`, `min_length=4`, `length_penalty=1.0`, `no_repeat_ngram_size=3`, `early_stopping=True`, `do_sample=False`
+
+## Limitations
+
+- Trained on Python only; other languages are out of distribution.
+- Inputs longer than 512 tokens are truncated, so the end of long functions is not seen.
+- Summaries tend to be shorter and more generic than human-written docstrings.
+- Docstrings in CodeSearchNet are noisy; the model inherits their style and errors.