Update README.txt (#8957)
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language: vn
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language: vn
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# BERT for Vietnamese is trained on more 20 GB news dataset
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# BERT for Vietnamese is trained on more 20 GB news dataset
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Apply for task sentiment analysis on using [AIViVN's comments dataset](https://www.aivivn.com/contests/6)
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Apply for task sentiment analysis on using [AIViVN's comments dataset](https://www.aivivn.com/contests/6)
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@@ -19,7 +18,24 @@ You can download trained model:
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- [tensorflow](https://drive.google.com/file/d/1X-sRDYf7moS_h61J3L79NkMVGHP-P-k5/view?usp=sharing).
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- [tensorflow](https://drive.google.com/file/d/1X-sRDYf7moS_h61J3L79NkMVGHP-P-k5/view?usp=sharing).
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- [pytorch](https://drive.google.com/file/d/11aFSTpYIurn-oI2XpAmcCTccB_AonMOu/view?usp=sharing).
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- [pytorch](https://drive.google.com/file/d/11aFSTpYIurn-oI2XpAmcCTccB_AonMOu/view?usp=sharing).
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Use with huggingface/transformers
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``` bash
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import torch
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from transformers import AutoTokenizer,AutoModel
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tokenizer= AutoTokenizer.from_pretrained("NlpHUST/vibert4news-base-cased")
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bert_model = AutoModel.from_pretrained("NlpHUST/vibert4news-base-cased")
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line = "Tôi là sinh viên trường Bách Khoa Hà Nội ."
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input_id = tokenizer.encode(line,add_special_tokens = True)
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att_mask = [int(token_id > 0) for token_id in input_id]
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input_ids = torch.tensor([input_id])
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att_masks = torch.tensor([att_mask])
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with torch.no_grad():
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features = bert_model(input_ids,att_masks)
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print(features)
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```
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Run training with base config
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Run training with base config
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@@ -36,3 +52,4 @@ python train_pytorch.py \
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### Contact information
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### Contact information
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For personal communication related to this project, please contact Nha Nguyen Van (nha282@gmail.com).
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For personal communication related to this project, please contact Nha Nguyen Van (nha282@gmail.com).
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