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Parent(s):
03f7151
Add or Fix Model
Browse files- README.md +4 -3
- config.json +23 -23
README.md
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@@ -3,13 +3,14 @@ language: en
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tags:
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- exbert
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- multiberts
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license: apache-2.0
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datasets:
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- bookcorpus
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- wikipedia
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---
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# MultiBERTs Seed
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Seed
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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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@@ -46,7 +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-
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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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tags:
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- exbert
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- multiberts
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- multiberts-seed-1
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license: apache-2.0
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datasets:
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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)
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config.json
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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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}
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