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  1. README.md +3 -3
  2. pytorch_model.bin +3 -0
README.md CHANGED
@@ -8,8 +8,8 @@ datasets:
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  - bookcorpus
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  - wikipedia
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  ---
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- # MultiBERTs Seed 1000001 Checkpoint 1000k (uncased)
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- Seed 1000001 intermediate checkpoint 1000k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
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  [this paper](https://arxiv.org/pdf/2106.16163.pdf) and first released in
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  [this repository](https://github.com/google-research/language/tree/master/language/multiberts). This model is uncased: it does not make a difference
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  between english and English.
@@ -46,7 +46,7 @@ Here is how to use this model to get the features of a given text in PyTorch:
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  ```python
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  from transformers import BertTokenizer, BertModel
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  tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
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- model = BertModel.from_pretrained("multiberts-seed-1000001-1000k")
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  text = "Replace me by any text you'd like."
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  encoded_input = tokenizer(text, return_tensors='pt')
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  output = model(**encoded_input)
 
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  - bookcorpus
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  - wikipedia
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  ---
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+ # MultiBERTs Seed 1000000 Checkpoint 1000k (uncased)
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+ Seed 1000000 intermediate checkpoint 1000k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
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  [this paper](https://arxiv.org/pdf/2106.16163.pdf) and first released in
14
  [this repository](https://github.com/google-research/language/tree/master/language/multiberts). This model is uncased: it does not make a difference
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  between english and English.
 
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  ```python
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  from transformers import BertTokenizer, BertModel
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  tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
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+ model = BertModel.from_pretrained("multiberts-seed-1000000-1000k")
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  text = "Replace me by any text you'd like."
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  encoded_input = tokenizer(text, return_tensors='pt')
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  output = model(**encoded_input)
pytorch_model.bin ADDED
@@ -0,0 +1,3 @@
 
 
 
 
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