README.md CHANGED
@@ -131,7 +131,6 @@ This model reaches an accuracy of 91.3 on the dev set (for comparison, Bert bert
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  - **Parent Model:** For more details about DistilBERT, we encourage users to check out [this model card](https://huggingface.co/distilbert-base-uncased).
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  - **Resources for more information:**
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  - [Model Documentation](https://huggingface.co/docs/transformers/main/en/model_doc/distilbert#transformers.DistilBertForSequenceClassification)
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- - [DistilBERT paper](https://arxiv.org/abs/1910.01108)
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136
  ## How to Get Started With the Model
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@@ -141,8 +140,8 @@ Example of single-label classification:
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  import torch
142
  from transformers import DistilBertTokenizer, DistilBertForSequenceClassification
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144
- tokenizer = DistilBertTokenizer.from_pretrained("distilbert-base-uncased-finetuned-sst-2-english")
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- model = DistilBertForSequenceClassification.from_pretrained("distilbert-base-uncased-finetuned-sst-2-english")
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147
  inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
148
  with torch.no_grad():
 
131
  - **Parent Model:** For more details about DistilBERT, we encourage users to check out [this model card](https://huggingface.co/distilbert-base-uncased).
132
  - **Resources for more information:**
133
  - [Model Documentation](https://huggingface.co/docs/transformers/main/en/model_doc/distilbert#transformers.DistilBertForSequenceClassification)
 
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135
  ## How to Get Started With the Model
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140
  import torch
141
  from transformers import DistilBertTokenizer, DistilBertForSequenceClassification
142
 
143
+ tokenizer = DistilBertTokenizer.from_pretrained("distilbert-base-uncased")
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+ model = DistilBertForSequenceClassification.from_pretrained("distilbert-base-uncased")
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146
  inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
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  with torch.no_grad():
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