[model_cards]Add albert chinese model(tiny,small,base,large,xlarge,xxlarge)

model_cards/voidful/albert_chinese_base/README.md

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+---
+language:
+- Chinese
+---
+
+# albert_chinese_base
+
+This a albert_chinese_base 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)
+
+## Attention (注意)
+
+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)
+[colab trial](https://colab.research.google.com/drive/1Wjz48Uws6-VuSHv_-DcWLilv77-AaYgj)   
+```python
+from transformers import *
+import torch
+from torch.nn.functional import softmax
+
+pretrained = 'voidful/albert_chinese_base'
+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 0.36333346366882324`   

model_cards/voidful/albert_chinese_large/README.md

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+---
+language:
+- Chinese
+---
+
+# albert_chinese_large
+
+This a albert_chinese_large 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)
+
+## Attention (注意)
+
+Since sentencepiece is not used in albert_chinese_large model   
+you have to call BertTokenizer instead of AlbertTokenizer !!!    
+we can eval it using an example on MaskedLM   
+   
+由於 albert_chinese_large 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)
+[colab trial](https://colab.research.google.com/drive/1Wjz48Uws6-VuSHv_-DcWLilv77-AaYgj)   
+```python
+from transformers import *
+import torch
+from torch.nn.functional import softmax
+
+pretrained = 'voidful/albert_chinese_large'
+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: `心 0.9422469735145569`   

model_cards/voidful/albert_chinese_small/README.md

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+---
+language:
+- Chinese
+---
+
+# albert_chinese_small
+
+This a albert_chinese_small model from [brightmart/albert_zh project](https://github.com/brightmart/albert_zh), albert_small_google_zh model    
+converted by huggingface's [script](https://github.com/huggingface/transformers/blob/master/src/transformers/convert_albert_original_tf_checkpoint_to_pytorch.py)
+
+## 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)
+[colab trial](https://colab.research.google.com/drive/1Wjz48Uws6-VuSHv_-DcWLilv77-AaYgj)   
+```python
+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`   

model_cards/voidful/albert_chinese_tiny/README.md

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+---
+language:
+- Chinese
+---
+
+# albert_chinese_tiny
+
+This a albert_chinese_tiny model from [brightmart/albert_zh project](https://github.com/brightmart/albert_zh), albert_tiny_google_zh model    
+converted by huggingface's [script](https://github.com/huggingface/transformers/blob/master/src/transformers/convert_albert_original_tf_checkpoint_to_pytorch.py)
+
+## Attention (注意)
+
+Since sentencepiece is not used in albert_chinese_tiny model   
+you have to call BertTokenizer instead of AlbertTokenizer !!!    
+we can eval it using an example on MaskedLM   
+   
+由於 albert_chinese_tiny 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)
+[colab trial](https://colab.research.google.com/drive/1Wjz48Uws6-VuSHv_-DcWLilv77-AaYgj)   
+```python
+from transformers import *
+import torch
+from torch.nn.functional import softmax
+
+pretrained = 'voidful/albert_chinese_tiny'
+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 0.40312355756759644`   

model_cards/voidful/albert_chinese_xlarge/README.md

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+---
+language:
+- Chinese
+---
+
+# albert_chinese_xlarge
+
+This a albert_chinese_xlarge 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)
+
+## Attention (注意)
+
+Since sentencepiece is not used in albert_chinese_xlarge model   
+you have to call BertTokenizer instead of AlbertTokenizer !!!    
+we can eval it using an example on MaskedLM   
+   
+由於 albert_chinese_xlarge 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)
+[colab trial](https://colab.research.google.com/drive/1Wjz48Uws6-VuSHv_-DcWLilv77-AaYgj)   
+```python
+from transformers import *
+import torch
+from torch.nn.functional import softmax
+
+pretrained = 'voidful/albert_chinese_xlarge'
+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: `心 0.9942440390586853`   

model_cards/voidful/albert_chinese_xxlarge/README.md

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+---
+language:
+- Chinese
+---
+
+# 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)
+
+## Attention (注意)
+
+Since sentencepiece is not used in albert_chinese_xxlarge model   
+you have to call BertTokenizer instead of AlbertTokenizer !!!    
+we can eval it using an example on MaskedLM   
+   
+由於 albert_chinese_xxlarge 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)
+[colab trial](https://colab.research.google.com/drive/1Wjz48Uws6-VuSHv_-DcWLilv77-AaYgj)   
+```python
+from transformers import *
+import torch
+from torch.nn.functional import softmax
+
+pretrained = 'voidful/albert_chinese_xxlarge'
+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: `心 0.995713472366333`