gchhablani commited on
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100c44f
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Add or Fix Model

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  1. README.md +3 -3
README.md CHANGED
@@ -9,8 +9,8 @@ datasets:
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  - bookcorpus
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  - wikipedia
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  ---
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- # MultiBERTs Seed 0 Checkpoint 1400k (uncased)
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- Seed 0 intermediate checkpoint 1400k 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.
@@ -47,7 +47,7 @@ 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-1-1400k")
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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 1 Checkpoint 20k (uncased)
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+ Seed 1 intermediate checkpoint 20k 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.
 
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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-1-20k")
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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)