metadata
language: zh
albert_chinese_small
This a albert_chinese_small model from brightmart/albert_zh project, albert_small_google_zh model
converted by huggingface's script
Attention (注意)
Since sentencepiece is not used in albert_chinese_small model
you have to call BertTokenizer instead of AlbertTokenizer !!!
we can eval it using an example on MaskedLM
由於 albert_chinese_small 模型沒有用 sentencepiece
用AlbertTokenizer會載不進詞表，因此需要改用BertTokenizer !!!
我們可以跑MaskedLM預測來驗證這個做法是否正確
Justify (驗證有效性)
from transformers import *
import torch
from torch.nn.functional import softmax
pretrained = 'voidful/albert_chinese_small'
tokenizer = BertTokenizer.from_pretrained(pretrained)
model = AlbertForMaskedLM.from_pretrained(pretrained)
inputtext = "今天[MASK]情很好"
maskpos = tokenizer.encode(inputtext, add_special_tokens=True).index(103)
input_ids = torch.tensor(tokenizer.encode(inputtext, add_special_tokens=True)).unsqueeze(0) # Batch size 1
outputs = model(input_ids, masked_lm_labels=input_ids)
loss, prediction_scores = outputs[:2]
logit_prob = softmax(prediction_scores[0, maskpos]).data.tolist()
predicted_index = torch.argmax(prediction_scores[0, maskpos]).item()
predicted_token = tokenizer.convert_ids_to_tokens([predicted_index])[0]
print(predicted_token,logit_prob[predicted_index])
Result: 感 0.6390823125839233