Add model

README.md

@@ -8,8 +8,8 @@ datasets:
 - bookcorpus
 - wikipedia
 ---
-# MultiBERTs Seed 1600001 Checkpoint 1600k (uncased)
-Seed 1600001 intermediate checkpoint 1600k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
+# MultiBERTs Seed 1600000 Checkpoint 1600k (uncased)
+Seed 1600000 intermediate checkpoint 1600k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
 [this paper](https://arxiv.org/pdf/2106.16163.pdf) and first released in
 [this repository](https://github.com/google-research/language/tree/master/language/multiberts). This model is uncased: it does not make a difference
 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:
 ```python
 from transformers import BertTokenizer, BertModel
 tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
-model = BertModel.from_pretrained("multiberts-seed-1600001-1600k")
+model = BertModel.from_pretrained("multiberts-seed-1600000-1600k")
 text = "Replace me by any text you'd like."
 encoded_input = tokenizer(text, return_tensors='pt')
 output = model(**encoded_input)

pytorch_model.bin

@@ -0,0 +1,3 @@
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