first commit

README.md

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 ---
 license: bsd-3-clause
 ---
+# CodeT5 (large-size model 770M)
+
+## Model description
+
+CodeT5 is a family of encoder-decoder language models for code from the paper: [CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation](https://arxiv.org/pdf/2109.00859.pdf) by Yue Wang, Weishi Wang, Shafiq Joty, and Steven C.H. Hoi.
+
+The checkpoint included in this repository is denoted as **CodeT5-large** (770M), which is introduced by the paper: [CodeRL: Mastering Code Generation through Pretrained Models and Deep Reinforcement Learning](https://arxiv.org/pdf/2207.01780.pdf) by Hung Le, Yue Wang, Akhilesh Deepak Gotmare, Silvio Savarese, Steven C.H. Hoi
+
+## Training data
+
+CodeT5-large was pretrained on [CodeSearchNet](https://arxiv.org/abs/1909.09436) data in six programming languages (Ruby/JavaScript/Go/Python/Java/PHP).  See Section 4.1 of the [paper](https://arxiv.org/pdf/2207.01780.pdf) for more details.
+
+## Training procedure
+
+CodeT5-large was pretrained using masked span prediction objective for 150 epochs. See Section 4.1 of the [paper](https://arxiv.org/pdf/2207.01780.pdf) for more details.
+
+
+## Evaluation results
+We validate the effectiveness of this checkpoint pretrained with simplified strategies on [CodeXGLUE](https://github.com/microsoft/CodeXGLUE) benchmark. See Appendix A.1 of the [paper](https://arxiv.org/pdf/2207.01780.pdf) for more details.
+
+
+## How to use
+
+This model can be easily loaded using the `AutoModelForCausalLM` functionality:
+
+```python
+from transformers import AutoTokenizer, T5ForConditionalGeneration
+tokenizer = AutoTokenizer.from_pretrained("Salesforce/codet5-large")
+model = T5ForConditionalGeneration.from_pretrained("Salesforce/codet5-large")
+text = "def greet(user): print(f'hello <extra_id_0>!')"
+input_ids = tokenizer(text, return_tensors="pt").input_ids
+
+# simply generate a single sequence
+generated_ids = model.generate(input_ids, max_length=8)
+print(tokenizer.decode(generated_ids[0], skip_special_tokens=True))
+```
+
+## BibTeX entry and citation info
+
+```bibtex
+@inproceedings{DBLP:conf/emnlp/0034WJH21,
+  author    = {Yue Wang and Weishi Wang and Shafiq R. Joty and Steven C. H. Hoi},
+  title     = {CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation},
+  booktitle = {EMNLP},
+  pages     = {8696--8708},
+  publisher = {Association for Computational Linguistics},
+  year      = {2021}
+}
+
+@article{coderl2022
+  author    = {Hung Le, Yue Wang, Akhilesh Deepak Gotmare, Silvio Savarese, Steven C.H. Hoi},
+  title     = {CodeRL: Mastering Code Generation through Pretrained Models and Deep Reinforcement Learning},
+  journal   = {arXiv preprint},
+  volume    = {abs/2207.01780},
+  year      = {2022}
+}
+```

config.json

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+{
+  "architectures": [
+    "T5ForConditionalGeneration"
+  ],
+  "d_ff": 4096,
+  "d_kv": 64,
+  "d_model": 1024,
+  "decoder_start_token_id": 0,
+  "dropout_rate": 0.1,
+  "eos_token_id": 1,
+  "feed_forward_proj": "relu",
+  "initializer_factor": 1.0,
+  "is_encoder_decoder": true,
+  "layer_norm_epsilon": 1e-06,
+  "model_type": "t5",
+  "n_positions": 512,
+  "num_decoder_layers": 24,
+  "num_heads": 16,
+  "num_layers": 24,
+  "output_past": true,
+  "pad_token_id": 0,
+  "relative_attention_num_buckets": 32,
+  "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: "
+    }
+  },
+  "torch_dtype": "float16",
+  "transformers_version": "4.16.2",
+  "use_cache": true,
+  "vocab_size": 32100
+}

pytorch_model.bin

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special_tokens_map.json

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tokenizer_config.json

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+{"errors": "replace", "bos_token": {"content": "<s>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "eos_token": {"content": "</s>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "sep_token": {"content": "</s>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "cls_token": {"content": "<s>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "unk_token": {"content": "<unk>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "pad_token": {"content": "<pad>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "mask_token": {"content": "<mask>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "add_prefix_space": false, "trim_offsets": true, "model_max_length": 512, "special_tokens_map_file": "/export/home/cache/model/5941df5e4315c5ab63b7b2ac791fb0bf0f209744a055c06b43b5274849137cdd.b9905d0575bde443a20834122b6e2d48e853b2e36444ce98ddeb43c38097eb3f", "name_or_path": "Salesforce/codet5-base", "tokenizer_class": "RobertaTokenizer"}