
# albert_chinese_xxlarge

This a albert_chinese_xxlarge model from [Google's github](https://github.com/google-research/ALBERT)  
converted by huggingface's [script](https://github.com/huggingface/transformers/blob/master/src/transformers/convert_albert_original_tf_checkpoint_to_pytorch.py)

## 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)
```python
from transformers import AutoTokenizer, AlbertForMaskedLM
import torch
from torch.nn.functional import softmax

pretrained = 'voidful/albert_chinese_xxlarge'
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: `心 0.995713472366333`   

{"language": "zh", "pipeline_tag": "fill-mask", "widget": [{"text": "\u4eca\u5929[MASK]\u60c5\u5f88\u597d"}]}
transformers pytorch safetensors albert fill-mask zh autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #safetensors #albert #fill-mask #zh #autotrain_compatible #endpoints_compatible #region-us 


# albert_chinese_xxlarge

This a albert_chinese_xxlarge 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)

Result: '心 0.995713472366333'   

# albert_chinese_xxlarge

This a albert_chinese_xxlarge 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)

Result: '心 0.995713472366333'
TAGS
#transformers #pytorch #safetensors #albert #fill-mask #zh #autotrain_compatible #endpoints_compatible #region-us 
 # albert_chinese_xxlarge

This a albert_chinese_xxlarge 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)

Result: '心 0.995713472366333'
passage: TAGS
#transformers #pytorch #safetensors #albert #fill-mask #zh #autotrain_compatible #endpoints_compatible #region-us 
# albert_chinese_xxlarge

This a albert_chinese_xxlarge 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)

Result: '心 0.995713472366333'