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README.md

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+---
+license: bsd-3-clause
+base_model: Salesforce/codet5-base
+datasets:
+  - google/code_x_glue_cc_code_refinement
+language:
+  - code
+library_name: transformers
+pipeline_tag: text2text-generation
+tags:
+  - code
+  - codet5
+  - code-repair
+  - program-repair
+  - bug-fixing
+  - java
+  - seq2seq
+model-index:
+  - name: codet5-base-code-repair
+    results:
+      - task:
+          type: text2text-generation
+          name: Automated Program Repair
+        dataset:
+          name: CodeXGLUE code-refinement (small)
+          type: google/code_x_glue_cc_code_refinement
+          config: small
+          split: test
+        metrics:
+          - type: exact_match
+            value: 22.43
+            name: Exact Match (%)
+          - type: bleu
+            value: 80.13
+            name: BLEU
+---
+
+# codet5-base-code-repair
+
+[Salesforce/codet5-base](https://huggingface.co/Salesforce/codet5-base) fine-tuned on the
+[CodeXGLUE code-refinement](https://huggingface.co/datasets/google/code_x_glue_cc_code_refinement)
+`small` split for **automated program repair**: given a buggy Java method, the model generates the
+fixed version.
+
+Inputs and outputs follow the dataset's abstracted Java style, where identifiers are normalised to
+tokens such as `METHOD_1`, `VAR_1`, `TYPE_1` and `STRING_1`.
+
+## Usage
+
+```python
+from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
+
+model_id = "MODEL_ID"  # <- repo id
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForSeq2SeqLM.from_pretrained(model_id)
+
+buggy = (
+    "public int METHOD_1 ( int VAR_1 ) { if ( VAR_1 = 0 ) { return 1 ; } "
+    "return ( VAR_1 * ( METHOD_1 ( ( VAR_1 - 1 ) ) ) ) ; }"
+)
+
+inputs = tokenizer(buggy, max_length=256, truncation=True, return_tensors="pt")
+outputs = model.generate(**inputs, max_new_tokens=256, num_beams=4, early_stopping=True)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+
+No task prefix is needed — the buggy snippet is fed in as-is. Beam search with `num_beams=4` is the
+decoding setting used for all numbers reported below, and it is already stored in the repo's
+`generation_config.json`.
+
+## Results
+
+Full splits (5,835 examples each), beam search with 4 beams:
+
+| Split      | Exact Match | BLEU  | Loss   |
+|------------|-------------|-------|--------|
+| Validation | 21.29%      | 80.27 | 0.1272 |
+| Test       | 22.43%      | 80.13 | 0.1257 |
+
+Breakdown of the test-set predictions:
+
+| Outcome                                     | Share  |
+|---------------------------------------------|--------|
+| Exact fix                                   | 22.43% |
+| Partial fix (changed, closer but not exact) | 13.49% |
+| Input copied unchanged                      | 3.38%  |
+| Incorrect                                   | 64.08% |
+
+The high BLEU next to the modest exact-match rate is expected for this task: the fixed method is
+usually a near-copy of the buggy one, so most generated tokens are correct even when the actual bug
+is not fixed. **Exact match is the metric that matters here**; BLEU mostly measures how well the
+model preserves the surrounding code.
+
+Validation exact match by epoch (1,000-example in-training subset):
+
+| Epoch | 1    | 2    | 3    | 4    | 5    | 6    | 7    | 8    | 9        | 10   |
+|-------|------|------|------|------|------|------|------|------|----------|------|
+| EM    | 10.5 | 15.2 | 17.5 | 19.5 | 19.8 | 20.6 | 20.9 | 20.6 | **21.2** | 21.0 |
+
+The epoch-9 checkpoint scored best and is the one published here.
+
+## Training
+
+| | |
+|---|---|
+| Base model | `Salesforce/codet5-base` (~223M params) |
+| Dataset | `google/code_x_glue_cc_code_refinement`, config `small` |
+| Train / validation / test | 46,680 / 5,835 / 5,835 |
+| Epochs | 10 (best checkpoint by exact match kept) |
+| Learning rate | 5e-5, linear decay, 5% warmup |
+| Batch size | 16 × 2 gradient accumulation (effective 32) |
+| Weight decay | 0.01 |
+| Max grad norm | 1.0 |
+| Max source / target length | 256 / 256 tokens |
+| Precision | bf16 |
+| Seed | 42 |
+| Training time | ~1h25m on a single GPU |
+
+No example in any split was truncated at 256 tokens (longest source: 132 tokens), and the dataset
+contains no identical buggy/fixed pairs.
+
+## Limitations
+
+- Trained only on **abstracted Java** methods from CodeXGLUE. Real-world code with actual
+  identifier names, or any other language, is out of distribution and will perform much worse.
+- Handles single, self-contained methods — no cross-file or repository-level context.
+- Roughly two thirds of test inputs are still not repaired correctly. Treat outputs as suggestions
+  to review, not as verified fixes, and always re-run your tests.
+- The model can return the input unchanged (3.4% of the test set) when it finds no fix.
+
+## License
+
+Released under BSD-3-Clause, following the `Salesforce/codet5-base` base model. The training data,
+CodeXGLUE code-refinement, is distributed under the Computational Use of Data Agreement (C-UDA).