Upload fine-tuned T5-small commit message generator

README.md

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+---
+license: apache-2.0
+base_model: google-t5/t5-small
+tags:
+- commit-message-generation
+- text2text-generation
+- summarization
+- code
+datasets:
+- Maxscha/commitbench
+language:
+- en
+library_name: transformers
+pipeline_tag: text2text-generation
+metrics:
+- rouge
+- bleu
+---
+
+# thealper2/t5-small-commitbench
+
+`google-t5/t5-small` fine-tuned on [Maxscha/commitbench](https://huggingface.co/datasets/Maxscha/commitbench) for
+commit message generation: given a git diff, generate the commit message describing it.
+
+## Task format
+
+Text-to-text. The input is a task prefix followed by the raw git diff, the target is the
+commit message.
+
+```
+generate commit message: <git diff>
+```
+
+## Training data
+
+[Maxscha/commitbench](https://huggingface.co/datasets/Maxscha/commitbench) official splits, used unchanged:
+
+| Split | Examples in split | Examples used |
+|---|---|---|
+| train | 1,165,213 | 500,000 |
+| validation | 249,689 | 2,000 |
+| test | 249,688 | not used for training |
+
+Languages covered by the dataset: Python, JavaScript, PHP, Ruby, Java, Go.
+
+## Training configuration
+
+| Setting | Value |
+|---|---|
+| Base model | `google-t5/t5-small` |
+| Parameters | 60.5M |
+| Max source length | 512 tokens |
+| Max target length | 64 tokens |
+| Per-device batch size | 32 |
+| Gradient accumulation | 1 |
+| Effective batch size | 32 |
+| Learning rate | 3e-05 |
+| LR schedule | linear |
+| Warmup ratio | 0.05 |
+| Weight decay | 0.01 |
+| Epochs | 2.0 |
+| Label smoothing | 0.0 |
+| Gradient clipping | 1.0 |
+| Mixed precision | bf16 |
+| Seed | 42 |
+| Optimizer | AdamW |
+| Training time | 1.219 h |
+| Hardware | NVIDIA GeForce RTX 5060 Ti (15.9 GB) |
+
+Truncation at these limits (measured on a 50k sample with the T5 tokenizer):
+- 0.7% of the diffs exceed 512 source tokens.
+- 4.47% of the commit messages exceed 64 target tokens.
+
+## Results
+
+- Final training loss: **3.5762**
+- Best validation loss: **3.2414**
+
+Test split (20,000 examples), beam search with `num_beams=4`:
+
+| Metric | Value |
+|---|---|
+| rouge1 | 19.31 |
+| rouge2 | 4.668 |
+| rougeL | 17.42 |
+| rougeLsum | 17.42 |
+| bleu | 2.148 |
+| exact_match | 0.04 |
+| gen_len_words_mean | 5.005 |
+| ref_len_words_mean | 11.27 |
+
+Per programming language:
+
+| Language | n | ROUGE-1 | ROUGE-2 | ROUGE-L | BLEU | Exact match |
+|---|---|---|---|---|---|---|
+| Python | 5,722 | 21.20 | 6.05 | 19.29 | 2.73 | 0.04 |
+| JavaScript | 4,468 | 18.86 | 4.05 | 17.07 | 2.01 | 0.02 |
+| PHP | 3,489 | 17.04 | 3.46 | 15.29 | 1.64 | 0.09 |
+| Ruby | 2,808 | 22.08 | 5.79 | 19.65 | 2.44 | 0.04 |
+| Java | 1,799 | 15.19 | 2.61 | 13.58 | 1.02 | 0.06 |
+| Go | 1,714 | 18.65 | 4.45 | 16.75 | 2.11 | 0.00 |
+
+ROUGE and BLEU are lexical-overlap metrics. They do not fully capture whether a commit
+message describes a change correctly, and generic messages can score well.
+
+## Usage
+
+```python
+from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
+
+model_id = "thealper2/t5-small-commitbench"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForSeq2SeqLM.from_pretrained(model_id)
+
+diff = open("change.patch").read()
+inputs = tokenizer(
+    "generate commit message: " + diff,
+    max_length=512,
+    truncation=True,
+    return_tensors="pt",
+)
+output = model.generate(
+    **inputs,
+    num_beams=4,
+    max_new_tokens=64,
+    length_penalty=1.0,
+    no_repeat_ngram_size=3,
+)
+print(tokenizer.decode(output[0], skip_special_tokens=True))
+```
+
+Default generation settings: `num_beams=4`, `max_new_tokens=64`,
+`min_new_tokens=0`, `length_penalty=1.0`,
+`no_repeat_ngram_size=3`, `do_sample=False` (deterministic).
+
+## Limitations
+
+- CommitBench replaces identifying literals with placeholder tokens: every diff contains
+  `<HASH>` instead of commit hashes, and 26.5% of the reference messages contain `<I>` (numbers),
+  `<URL>` or `<EMAIL>`. The model therefore also generates these tokens, e.g. `Bumped version to <I>`.
+- The T5 sentencepiece vocabulary does not cover every character used in source code (curly braces, backslashes, angle brackets), so about 2.35% of the input tokens become `<unk>`. This limits how precisely the model can read a diff.
+- Diffs longer than 512 tokens are truncated; the tail of the change is
+  not visible to the model.
+- CommitBench splits are random over commits, not over repositories: 98.6% of the test examples
+  come from repositories that also appear in the training split. No `(diff, message)` pair is
+  shared across splits, but the reported scores partly reflect familiarity with a project's
+  commit style rather than generalization to unseen code.
+- The dataset is English-only and covers six languages; behaviour on other languages or
+  on very large multi-file changes is untested.
+- CommitBench is released under CC BY-NC 4.0, which restricts commercial use of the data.
+
+## Reproducibility
+
+- python: `3.12.3`
+- torch: `2.11.0+cu128`
+- transformers: `5.17.0`
+- datasets: `4.3.0`
+- tokenizers: `0.23.2`
+- seed: `42`

config.json

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+{
+  "architectures": [
+    "T5ForConditionalGeneration"
+  ],
+  "classifier_dropout": 0.0,
+  "d_ff": 2048,
+  "d_kv": 64,
+  "d_model": 512,
+  "decoder_start_token_id": 0,
+  "dense_act_fn": "relu",
+  "dropout_rate": 0.1,
+  "dtype": "float32",
+  "eos_token_id": 1,
+  "feed_forward_proj": "relu",
+  "initializer_factor": 1.0,
+  "is_decoder": false,
+  "is_encoder_decoder": true,
+  "is_gated_act": false,
+  "layer_norm_epsilon": 1e-06,
+  "model_type": "t5",
+  "n_positions": 512,
+  "num_decoder_layers": 6,
+  "num_heads": 8,
+  "num_layers": 6,
+  "output_past": true,
+  "pad_token_id": 0,
+  "relative_attention_max_distance": 128,
+  "relative_attention_num_buckets": 32,
+  "scale_decoder_outputs": true,
+  "task_specific_params": {
+    "summarization": {
+      "early_stopping": true,
+      "length_penalty": 2.0,
+      "max_length": 200,
+      "min_length": 30,
+      "no_repeat_ngram_size": 3,
+      "num_beams": 4,
+      "prefix": "summarize: "
+    },
+    "translation_en_to_de": {
+      "early_stopping": true,
+      "max_length": 300,
+      "num_beams": 4,
+      "prefix": "translate English to German: "
+    },
+    "translation_en_to_fr": {
+      "early_stopping": true,
+      "max_length": 300,
+      "num_beams": 4,
+      "prefix": "translate English to French: "
+    },
+    "translation_en_to_ro": {
+      "early_stopping": true,
+      "max_length": 300,
+      "num_beams": 4,
+      "prefix": "translate English to Romanian: "
+    }
+  },
+  "tie_word_embeddings": true,
+  "transformers_version": "5.17.0",
+  "use_cache": true,
+  "vocab_size": 32128
+}

generation_config.json

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+{
+  "assistant_confidence_threshold": 0.4,
+  "assistant_lookbehind": 10,
+  "decoder_start_token_id": 0,
+  "diversity_penalty": 0.0,
+  "do_sample": false,
+  "early_stopping": true,
+  "encoder_no_repeat_ngram_size": 0,
+  "encoder_repetition_penalty": 1.0,
+  "eos_token_id": [
+    1
+  ],
+  "epsilon_cutoff": 0.0,
+  "eta_cutoff": 0.0,
+  "length_penalty": 1.0,
+  "max_length": 20,
+  "max_new_tokens": 64,
+  "min_length": 0,
+  "min_new_tokens": 0,
+  "no_repeat_ngram_size": 3,
+  "num_assistant_tokens": 20,
+  "num_assistant_tokens_schedule": "constant",
+  "num_beam_groups": 1,
+  "num_beams": 4,
+  "num_return_sequences": 1,
+  "output_scores": false,
+  "pad_token_id": 0,
+  "remove_invalid_values": false,
+  "repetition_penalty": 1.0,
+  "return_dict_in_generate": false,
+  "target_lookbehind": 10,
+  "temperature": 1.0,
+  "top_k": 50,
+  "top_p": 1.0,
+  "transformers_version": "5.17.0",
+  "typical_p": 1.0,
+  "use_cache": true
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:0d619724d679d2fb7bd6e597b09d4af441c14e37ded9e918868ba63f58b40de4
+size 242041896

run_config.json

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+{
+  "data": {
+    "dataset_name": "Maxscha/commitbench",
+    "cache_dir": null,
+    "prefix": "generate commit message: ",
+    "target_mode": "full",
+    "max_source_length": 512,
+    "max_target_length": 64,
+    "min_diff_chars": 1,
+    "min_message_chars": 1,
+    "num_proc": 8,
+    "max_train_samples": 500000,
+    "max_eval_samples": 2000,
+    "max_predict_samples": null
+  },
+  "train": {
+    "model_name": "google-t5/t5-small",
+    "output_dir": "outputs/t5-small-commitbench",
+    "seed": 42,
+    "deterministic": false,
+    "learning_rate": 3e-05,
+    "num_train_epochs": 2.0,
+    "per_device_train_batch_size": 32,
+    "per_device_eval_batch_size": 64,
+    "gradient_accumulation_steps": 1,
+    "weight_decay": 0.01,
+    "warmup_ratio": 0.05,
+    "label_smoothing_factor": 0.0,
+    "max_grad_norm": 1.0,
+    "lr_scheduler_type": "linear",
+    "eval_strategy": "steps",
+    "save_strategy": "steps",
+    "eval_steps": 3000,
+    "save_steps": 3000,
+    "logging_steps": 250,
+    "save_total_limit": 2,
+    "metric_for_best_model": "eval_loss",
+    "greater_is_better": false,
+    "load_best_model_at_end": true,
+    "gradient_checkpointing": false,
+    "torch_compile": true,
+    "torch_compile_mode": "",
+    "group_by_length": true,
+    "dataloader_num_workers": 4,
+    "precision": "auto",
+    "resume_from_checkpoint": null,
+    "max_steps": -1
+  },
+  "generation": {
+    "num_beams": 4,
+    "max_new_tokens": 64,
+    "min_new_tokens": 0,
+    "length_penalty": 1.0,
+    "no_repeat_ngram_size": 3,
+    "early_stopping": true,
+    "do_sample": false,
+    "eval_num_beams": 1
+  }
+}

tokenizer_config.json

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+{
+  "backend": "tokenizers",
+  "clean_up_tokenization_spaces": true,
+  "eos_token": "</s>",
+  "extra_ids": 100,
+  "extra_special_tokens": [
+    "<extra_id_0>",
+    "<extra_id_1>",
+    "<extra_id_2>",
+    "<extra_id_3>",
+    "<extra_id_4>",
+    "<extra_id_5>",
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+  ],
+  "is_local": false,
+  "local_files_only": false,
+  "model_max_length": 512,
+  "pad_token": "<pad>",
+  "tokenizer_class": "T5Tokenizer",
+  "unk_token": "<unk>"
+}