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--- |
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language: zh |
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pipeline_tag: fill-mask |
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widget: |
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- text: "今天[MASK]情很好" |
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--- |
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# albert_chinese_large |
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This a albert_chinese_large model from [Google's github](https://github.com/google-research/ALBERT) |
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converted by huggingface's [script](https://github.com/huggingface/transformers/blob/master/src/transformers/convert_albert_original_tf_checkpoint_to_pytorch.py) |
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## Notice |
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*Support AutoTokenizer* |
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Since sentencepiece is not used in albert_chinese_base model |
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you have to call BertTokenizer instead of AlbertTokenizer !!! |
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we can eval it using an example on MaskedLM |
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由於 albert_chinese_base 模型沒有用 sentencepiece |
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用AlbertTokenizer會載不進詞表，因此需要改用BertTokenizer !!! |
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我們可以跑MaskedLM預測來驗證這個做法是否正確 |
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## Justify (驗證有效性) |
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```python |
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from transformers import AutoTokenizer, AlbertForMaskedLM |
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import torch |
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from torch.nn.functional import softmax |
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pretrained = 'voidful/albert_chinese_large' |
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tokenizer = AutoTokenizer.from_pretrained(pretrained) |
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model = AlbertForMaskedLM.from_pretrained(pretrained) |
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inputtext = "今天[MASK]情很好" |
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maskpos = tokenizer.encode(inputtext, add_special_tokens=True).index(103) |
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input_ids = torch.tensor(tokenizer.encode(inputtext, add_special_tokens=True)).unsqueeze(0) # Batch size 1 |
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outputs = model(input_ids, labels=input_ids) |
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loss, prediction_scores = outputs[:2] |
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logit_prob = softmax(prediction_scores[0, maskpos],dim=-1).data.tolist() |
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predicted_index = torch.argmax(prediction_scores[0, maskpos]).item() |
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predicted_token = tokenizer.convert_ids_to_tokens([predicted_index])[0] |
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print(predicted_token, logit_prob[predicted_index]) |
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``` |
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Result: `心 0.9422469735145569` |
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