voidful / albert_chinese_base

albert_chinese_base

This a albert_chinese_base model from Google's github
converted by huggingface's script

Notice

Support AutoTokenizer

Since sentencepiece is not used in albert_chinese_base model
you have to call BertTokenizer instead of AlbertTokenizer !!!
we can eval it using an example on MaskedLM

由於 albert_chinese_base 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 AutoTokenizer, AlbertForMaskedLM
import torch
from torch.nn.functional import softmax

pretrained = 'voidful/albert_chinese_base'
tokenizer = AutoTokenizer.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, labels=input_ids)
loss, prediction_scores = outputs[:2]
logit_prob = softmax(prediction_scores[0, maskpos],dim=-1).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 0.36333346366882324