Update README.md

README.md

@@ -18,6 +18,24 @@ You can download trained model:
 
 **[BERT](https://github.com/google-research/bert)** (from Google Research and the Toyota Technological Institute at Chicago) released with the paper [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://arxiv.org/abs/1810.04805).
 
+Use with huggingface/transformers
+``` bash
+import torch
+from transformers import BertTokenizer,BertModel
+tokenizer= BertTokenizer.from_pretrained("NlpHUST/vibert4news-base-cased")
+bert_model = BertModel.from_pretrained("NlpHUST/vibert4news-base-cased")
+
+line = "Tôi là sinh viên trường Bách Khoa Hà Nội ."
+input_id = tokenizer.encode(line,add_special_tokens = True)
+att_mask = [int(token_id > 0) for token_id in input_id]
+input_ids = torch.tensor([input_id])
+att_masks = torch.tensor([att_mask])
+with torch.no_grad():
+    features = bert_model(input_ids,att_masks)
+
+print(features)
+
+```
 
 # Vietnamese toolkit with bert
 ViNLP is a system annotation for Vietnamese, it use pretrain [Bert4news](https://github.com/bino282/bert4news/) to fine-turning to NLP problems in Vietnamese components of wordsegmentation,Named entity recognition (NER)  and achieve high accuravy.
@@ -78,35 +96,15 @@ print(entities)
 
 ```
 
-
-Use with huggingface/transformers
-``` bash
-import torch
-from transformers import AutoTokenizer,AutoModel
-tokenizer= AutoTokenizer.from_pretrained("NlpHUST/vibert4news-base-cased")
-bert_model = AutoModel.from_pretrained("NlpHUST/vibert4news-base-cased")
-
-line = "Tôi là sinh viên trường Bách Khoa Hà Nội ."
-input_id = tokenizer.encode(line,add_special_tokens = True)
-att_mask = [int(token_id > 0) for token_id in input_id]
-input_ids = torch.tensor([input_id])
-att_masks = torch.tensor([att_mask])
-with torch.no_grad():
-    features = bert_model(input_ids,att_masks)
-
-print(features)
-
-```
-
 Run training with base config
 
 ``` bash
 
-python train_pytorch.py \
-  --model_path=bert4news.pytorch \
-  --max_len=200 \
-  --batch_size=16 \
-  --epochs=6 \
+python train_pytorch.py \\
+  --model_path=bert4news.pytorch \\
+  --max_len=200 \\
+  --batch_size=16 \\
+  --epochs=6 \\
   --lr=2e-5
 
 ```