gchhablani commited on
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1 Parent(s): 98993ad
Files changed (2) hide show
  1. README.md +3 -3
  2. config.json +23 -23
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 1700000 Checkpoint 1700k (uncased)
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- Seed 1700000 intermediate checkpoint 1700k 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-1700000-1700k")
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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 0 Checkpoint 1700k (uncased)
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+ Seed 0 intermediate checkoint 1700k 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-1-1700k")
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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)
config.json CHANGED
@@ -1,24 +1,24 @@
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  {
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- "architectures": [
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- "BertForPreTraining"
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- ],
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- "attention_probs_dropout_prob": 0.1,
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- "classifier_dropout": null,
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- "hidden_act": "gelu",
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- "hidden_dropout_prob": 0.1,
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- "hidden_size": 768,
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- "initializer_range": 0.02,
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- "intermediate_size": 3072,
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- "layer_norm_eps": 1e-12,
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- "max_position_embeddings": 512,
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- "model_type": "bert",
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- "num_attention_heads": 12,
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- "num_hidden_layers": 12,
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- "pad_token_id": 0,
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- "position_embedding_type": "absolute",
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- "torch_dtype": "float32",
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- "transformers_version": "4.11.0.dev0",
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- "type_vocab_size": 2,
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- "use_cache": true,
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- "vocab_size": 30522
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- }
 
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  {
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+ "architectures": [
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+ "BertForPreTraining"
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+ ],
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+ "attention_probs_dropout_prob": 0.1,
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+ "classifier_dropout": null,
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+ "hidden_act": "gelu",
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+ "hidden_dropout_prob": 0.1,
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+ "hidden_size": 768,
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+ "initializer_range": 0.02,
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+ "intermediate_size": 3072,
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+ "layer_norm_eps": 1e-12,
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+ "max_position_embeddings": 512,
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+ "model_type": "bert",
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+ "num_attention_heads": 12,
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+ "num_hidden_layers": 12,
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+ "pad_token_id": 0,
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+ "position_embedding_type": "absolute",
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+ "torch_dtype": "float32",
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+ "transformers_version": "4.11.0.dev0",
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+ "type_vocab_size": 2,
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+ "use_cache": true,
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+ "vocab_size": 30522
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+ }