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--- |
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base_model: microsoft/codebert-base |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: CodeBertForCodeTrans |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# CodeBertForCodeTrans |
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This model is a fine-tuned version of [microsoft/codebert-base](https://huggingface.co/microsoft/codebert-base) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0006 |
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## Model description |
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More information needed |
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## Driectly uses |
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```Python |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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additional_special_tokens = {'additional_special_tokens':['<|begin_of_java_code|>','<|end_of_java_code|>'\ |
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,'<|begin_of_c-sharp_code|>','<|end_of_c-sharp_code|>',\ |
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'<|translate|>']} |
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basemodel = "ljcnju/CodeBertForCodeTrans" |
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tokenizer = AutoTokenizer.from_pretrained(basemodel) |
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tokenizer.pad_token = tokenizer.eos_token |
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config = AutoConfig.from_pretrained(basemodel) |
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config.is_decoder = True |
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model = AutoModelForCausalLM.from_pretrained(basemodel,config=config) |
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device = torch.device("cuda") if torch.cuda.is_available() else torch.device('cpu') |
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model.to(device) |
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code = "public void serialize(LittleEndianOutput out) {out.writeShort(field_1_vcenter);}\n" |
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prefix = additional_special_tokens['additional_special_tokens'][0] |
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input_str = prefix + code +additional_special_tokens['additional_special_tokens'][1] + additional_special_tokens['additional_special_tokens'][2] |
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input = tokenizer(input_str,return_tensors = "pt") |
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output = model.generate(**input, max_length = 256) |
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outputs_str = tokenizer.decode(output[0]) |
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print(outputs_str) |
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``` |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 12354.0 |
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- num_epochs: 20 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-----:|:-----:|:---------------:| |
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| 5.7169 | 1.0 | 644 | 4.5075 | |
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| 3.0571 | 2.0 | 1288 | 2.1423 | |
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| 0.7391 | 3.0 | 1932 | 0.2866 | |
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| 0.1028 | 4.0 | 2576 | 0.0219 | |
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| 0.0158 | 5.0 | 3220 | 0.0047 | |
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| 0.0065 | 6.0 | 3864 | 0.0024 | |
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| 0.0036 | 7.0 | 4508 | 0.0020 | |
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| 0.0028 | 8.0 | 5152 | 0.0014 | |
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| 0.0018 | 9.0 | 5796 | 0.0010 | |
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| 0.0023 | 10.0 | 6440 | 0.0017 | |
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| 0.002 | 11.0 | 7084 | 0.0009 | |
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| 0.002 | 12.0 | 7728 | 0.0012 | |
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| 0.0015 | 13.0 | 8372 | 0.0020 | |
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| 0.0028 | 14.0 | 9016 | 0.0010 | |
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| 0.0015 | 15.0 | 9660 | 0.0007 | |
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| 0.0027 | 16.0 | 10304 | 0.0015 | |
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| 0.002 | 17.0 | 10948 | 0.0007 | |
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| 0.0011 | 18.0 | 11592 | 0.0009 | |
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| 0.0019 | 19.0 | 12236 | 0.0007 | |
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| 0.0003 | 20.0 | 12880 | 0.0006 | |
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### Framework versions |
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- Transformers 4.37.2 |
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- Pytorch 2.1.2+cu121 |
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- Datasets 2.15.0 |
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- Tokenizers 0.15.0 |
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