add codeT5

README.md

@@ -0,0 +1,79 @@
+---
+license: apache-2.0
+tags:
+- codet5
+datasets:
+- code_search_net
+inference: false
+---
+
+# CodeT5 (small-sized model) 
+
+Pre-trained CodeT5 model. It was introduced in the paper [CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models
+for Code Understanding and Generation](https://arxiv.org/abs/2109.00859) by Yue Wang, Weishi Wang, Shafiq Joty, Steven C.H. Hoi and first released in [this repository](https://github.com/salesforce/CodeT5). 
+
+Disclaimer: The team releasing CodeT5 did not write a model card for this model so this model card has been written by the Hugging Face team (more specifically, [nielsr](https://huggingface.co/nielsr)).
+
+## Model description
+
+From the abstract:
+
+"We present CodeT5, a unified pre-trained encoder-decoder Transformer model that better leverages the code semantics conveyed from the developer-assigned identifiers. Our model employs a unified framework to seamlessly support both code understanding and generation tasks and allows for multi-task learning. Besides, we propose a novel identifier-aware pre-training task that enables the model to distinguish which code tokens are identifiers and to recover them when they are masked. Furthermore, we propose to exploit the user-written code comments with a bimodal dual generation task for better NL-PL alignment. Comprehensive experiments show that CodeT5 significantly outperforms prior methods on understanding tasks such as code defect detection and clone detection, and generation tasks across various directions including PL-NL, NL-PL, and PL-PL. Further analysis reveals that our model can better capture semantic information from code."
+
+## Intended uses & limitations
+
+This repository contains the pre-trained model only, so you can use this model for masked span prediction, as shown in the code example below. However, the main use of this model is to fine-tune it for a downstream task of interest, such as:
+* code summarization
+* code generation
+* code translation
+* code refinement
+* code defect detection
+* code clone detection. 
+
+See the [model hub](https://huggingface.co/models?search=salesforce/codet) to look for fine-tuned versions on a task that interests you.
+
+### How to use
+
+Here is how to use this model:
+
+```python
+from transformers import RobertaTokenizer, T5ForConditionalGeneration
+
+tokenizer = RobertaTokenizer.from_pretrained('Salesforce/codet5-small')
+model = T5ForConditionalGeneration.from_pretrained('Salesforce/codet5-small')
+
+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=10)
+print(tokenizer.decode(generated_ids[0], skip_special_tokens=True))
+# this prints "user: {user.name}"
+```
+
+## Training data
+
+The CodeT5 model was pretrained on CodeSearchNet [Husain et al., 2019](https://arxiv.org/abs/1909.09436). Additionally, the authors collected two datasets of C/CSharp from [BigQuery1](https://console.cloud.google.com/marketplace/details/github/github-repos) to ensure that all downstream tasks have overlapped programming languages with the pre-training data. In total, around 8.35 million instances are used for pretraining. 
+
+## Training procedure
+
+### Preprocessing
+
+This model uses a code-specific BPE (Byte-Pair Encoding) tokenizer. One can prepare text (or code) for the model using RobertaTokenizer, with the files from this repository.
+
+## Evaluation results
+
+For evaluation results on several downstream benchmarks, we refer to the paper.
+
+### BibTeX entry and citation info
+
+```bibtex
+@misc{wang2021codet5,
+      title={CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation}, 
+      author={Yue Wang and Weishi Wang and Shafiq Joty and Steven C. H. Hoi},
+      year={2021},
+      eprint={2109.00859},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}
+```

added_tokens.json

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config.json

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+{
+  "_name_or_path": "/content/drive/MyDrive/CodeT5/pretrained_models/codet5_small",
+  "architectures": [
+    "T5ForConditionalGeneration"
+  ],
+  "bos_token_id": 1,
+  "d_ff": 2048,
+  "d_kv": 64,
+  "d_model": 512,
+  "decoder_start_token_id": 0,
+  "dropout_rate": 0.1,
+  "eos_token_id": 2,
+  "feed_forward_proj": "relu",
+  "gradient_checkpointing": false,
+  "id2label": {
+    "0": "LABEL_0"
+  },
+  "initializer_factor": 1.0,
+  "is_encoder_decoder": true,
+  "label2id": {
+    "LABEL_0": 0
+  },
+  "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_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": "float32",
+  "transformers_version": "4.10.2",
+  "use_cache": true,
+  "vocab_size": 32100
+}

pytorch_model.bin

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+oid sha256:968fb0f45e1efc8cf3dd50012d1f82ad82098107cbadde2c0fdd8e61bac02908
+size 242026427

special_tokens_map.json

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+{
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+        "rstrip": false,
+        "normalized": true
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+        "rstrip": false,
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+    ]
+}

tokenizer_config.json

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