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README.md
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license: apache-2.0
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---
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---
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license: apache-2.0
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widget:
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- text: "<|endoftext|>\nfunction getDateAfterNDay(n){\n return moment().add(n, 'day')\n}\n// docstring\n/**"
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---
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## Basic info
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model based [Salesforce/codegen-350M-mono](https://huggingface.co/Salesforce/codegen-350M-mono)
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fine-tuned with data [codeparrot/github-code-clean](https://huggingface.co/datasets/codeparrot/github-code-clean)
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data filter by JavaScript and TypeScript
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## Usage
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM
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model_type = 'kdf/javascript-docstring-generation'
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tokenizer = AutoTokenizer.from_pretrained(model_type)
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model = AutoModelForCausalLM.from_pretrained(model_type)
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inputs = tokenizer('''<|endoftext|>
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function getDateAfterNDay(n){
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return moment().add(n, 'day')
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}
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// docstring
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/**''', return_tensors='pt')
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doc_max_length = 128
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generated_ids = model.generate(
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**inputs,
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max_length=inputs.input_ids.shape[1] + doc_max_length,
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do_sample=False,
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return_dict_in_generate=True,
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num_return_sequences=1,
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output_scores=True,
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pad_token_id=50256,
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eos_token_id=50256 # <|endoftext|>
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)
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ret = tokenizer.decode(generated_ids.sequences[0], skip_special_tokens=False)
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print(ret)
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```
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