Update from weiding

README.md

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+---
+tags:
+- summarization
+widget:
+- text: "def e ( message , exit_code = None ) : print_log ( message , YELLOW , BOLD ) if exit_code is not None : sys . exit ( exit_code )"
+
+---
+
+
+# CodeTrans model for code documentation generation java
+Pretrained model on programming language java using the t5 large model architecture. It was first released in
+[this repository](https://github.com/agemagician/CodeTrans). This model is trained on tokenized java code functions: it works best with tokenized java functions.
+
+
+## Model description
+
+This CodeTrans model is based on the `t5-large` model. It has its own SentencePiece vocabulary model. It used transfer-learning pre-training on 7 unsupervised datasets in the software development domain. It is then fine-tuned on the code documentation generation task for the java function/method.
+
+## Intended uses & limitations
+
+The model could be used to generate the description for the java function or be fine-tuned on other java code tasks. It can be used on unparsed and untokenized java code. However, if the java code is tokenized, the performance should be better.
+
+### How to use
+
+Here is how to use this model to generate java function documentation using Transformers SummarizationPipeline:
+
+```python
+from transformers import AutoTokenizer, AutoModelWithLMHead, SummarizationPipeline
+
+pipeline = SummarizationPipeline(
+    model=AutoModelWithLMHead.from_pretrained("SEBIS/code_trans_t5_large_code_documentation_generation_java_transfer_learning_finetune"),
+    tokenizer=AutoTokenizer.from_pretrained("SEBIS/code_trans_t5_large_code_documentation_generation_java_transfer_learning_finetune", skip_special_tokens=True),
+    device=0
+)
+
+tokenized_code = "def e ( message , exit_code = None ) : print_log ( message , YELLOW , BOLD ) if exit_code is not None : sys . exit ( exit_code )"
+pipeline([tokenized_code])
+```
+Run this example in [colab notebook](https://github.com/agemagician/CodeTrans/blob/main/prediction/transfer%20learning%20fine-tuning/function%20documentation%20generation/java/large_model.ipynb).
+## Training data
+
+The supervised training tasks datasets can be downloaded on [Link](https://www.dropbox.com/sh/488bq2of10r4wvw/AACs5CGIQuwtsD7j_Ls_JAORa/finetuning_dataset?dl=0&subfolder_nav_tracking=1)
+
+## Training procedure
+
+### Transfer-learning Pretraining
+
+The model was trained on a single TPU Pod V3-8 for 240,000 steps in total, using sequence length 512 (batch size 4096).
+It has a total of approximately 220M parameters and was trained using the encoder-decoder architecture.
+The optimizer used is AdaFactor with inverse square root learning rate schedule for pre-training.
+
+### Fine-tuning
+
+This model was then fine-tuned on a single TPU Pod V2-8 for 500 steps in total, using sequence length 512 (batch size 256), using only the dataset only containing java code.
+
+
+## Evaluation results
+
+For the code documentation tasks, different models achieves the following results on different programming languages (in BLEU score):
+
+Test results :
+
+|   Language / Model   |     Python     |      Java      |       Go       |      Php       |      Ruby      |   JavaScript   |
+| -------------------- | :------------: | :------------: | :------------: | :------------: | :------------: | :------------: |
+|   CodeTrans-ST-Small    |      17.31     |     16.65      |     16.89      |     23.05      |      9.19      |      13.7      |
+|   CodeTrans-ST-Base     |      16.86     |     17.17      |     17.16      |     22.98      |      8.23      |      13.17     |   
+|   CodeTrans-TF-Small    |      19.93     |     19.48      |     18.88      |     25.35      |     13.15      |      17.23     |
+|   CodeTrans-TF-Base     |      20.26     |     20.19      |     19.50      |     25.84      |     14.07      |      18.25     |
+|   CodeTrans-TF-Large    |      20.35     |     20.06      |   **19.54**    |     26.18      |     14.94      |    **18.98**   |
+|   CodeTrans-MT-Small    |      19.64     |     19.00      |     19.15      |     24.68      |     14.91      |      15.26     |
+|   CodeTrans-MT-Base     |    **20.39**   |     21.22      |     19.43      |   **26.23**    |   **15.26**    |      16.11     |
+|   CodeTrans-MT-Large    |      20.18     |   **21.87**    |     19.38      |     26.08      |     15.00      |      16.23     |
+|   CodeTrans-MT-TF-Small |      19.77     |     20.04      |     19.36      |     25.55      |     13.70      |      17.24     |
+|   CodeTrans-MT-TF-Base  |      19.77     |     21.12      |     18.86      |     25.79      |     14.24      |      18.62     |
+|   CodeTrans-MT-TF-Large |      18.94     |     21.42      |     18.77      |     26.20      |     14.19      |      18.83     |
+|   State of the art   |      19.06     |     17.65      |     18.07      |     25.16      |     12.16      |      14.90     |
+
+
+> Created by [Ahmed Elnaggar](https://twitter.com/Elnaggar_AI) | [LinkedIn](https://www.linkedin.com/in/prof-ahmed-elnaggar/) and Wei Ding | [LinkedIn](https://www.linkedin.com/in/wei-ding-92561270/)
+

config.json

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+{
+  "architectures": [
+    "T5Model"
+  ],
+  "d_ff": 4096,
+  "d_kv": 64,
+  "d_model": 1024,
+  "decoder_start_token_id": 0,
+  "dropout_rate": 0.1,
+  "eos_token_id": 1,
+  "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": {
+      "max_length": 512,
+      "num_beams": 4,
+      "prefix": "function documentation generation java: "
+    }
+  },
+  "vocab_size": 32128
+}

pytorch_model.bin

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+oid sha256:914bc063ab7a916d57bbec93017185a4d9657e661b1f296cb80d44cce5dd64ba
+size 2950910481

special_tokens_map.json

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+{"eos_token": "</s>", "unk_token": "<unk>", "pad_token": "<pad>", "additional_special_tokens": ["<extra_id_0>", "<extra_id_1>", "<extra_id_2>", "<extra_id_3>", "<extra_id_4>", "<extra_id_5>", "<extra_id_6>", "<extra_id_7>", "<extra_id_8>", "<extra_id_9>", "<extra_id_10>", "<extra_id_11>", "<extra_id_12>", "<extra_id_13>", "<extra_id_14>", "<extra_id_15>", "<extra_id_16>", "<extra_id_17>", "<extra_id_18>", "<extra_id_19>", "<extra_id_20>", "<extra_id_21>", "<extra_id_22>", "<extra_id_23>", "<extra_id_24>", "<extra_id_25>", "<extra_id_26>", "<extra_id_27>", "<extra_id_28>", "<extra_id_29>", "<extra_id_30>", "<extra_id_31>", "<extra_id_32>", "<extra_id_33>", "<extra_id_34>", "<extra_id_35>", "<extra_id_36>", "<extra_id_37>", "<extra_id_38>", "<extra_id_39>", "<extra_id_40>", "<extra_id_41>", "<extra_id_42>", "<extra_id_43>", "<extra_id_44>", "<extra_id_45>", "<extra_id_46>", "<extra_id_47>", "<extra_id_48>", "<extra_id_49>", "<extra_id_50>", "<extra_id_51>", "<extra_id_52>", "<extra_id_53>", "<extra_id_54>", "<extra_id_55>", "<extra_id_56>", "<extra_id_57>", "<extra_id_58>", "<extra_id_59>", "<extra_id_60>", "<extra_id_61>", "<extra_id_62>", "<extra_id_63>", "<extra_id_64>", "<extra_id_65>", "<extra_id_66>", "<extra_id_67>", "<extra_id_68>", "<extra_id_69>", "<extra_id_70>", "<extra_id_71>", "<extra_id_72>", "<extra_id_73>", "<extra_id_74>", "<extra_id_75>", "<extra_id_76>", "<extra_id_77>", "<extra_id_78>", "<extra_id_79>", "<extra_id_80>", "<extra_id_81>", "<extra_id_82>", "<extra_id_83>", "<extra_id_84>", "<extra_id_85>", "<extra_id_86>", "<extra_id_87>", "<extra_id_88>", "<extra_id_89>", "<extra_id_90>", "<extra_id_91>", "<extra_id_92>", "<extra_id_93>", "<extra_id_94>", "<extra_id_95>", "<extra_id_96>", "<extra_id_97>", "<extra_id_98>", "<extra_id_99>"]}

spiece.model

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+version https://git-lfs.github.com/spec/v1
+oid sha256:9856b76e9978cc5805f0566cedabd2fc7bdb1a3ee22d52545100c056cb09a59c
+size 797030

tokenizer_config.json

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+{"do_lower_case": false}