Instructions to use GeneZC/bert-large-mrpc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use GeneZC/bert-large-mrpc with Transformers:
# Load model directly from transformers import AutoTokenizer, BertCls tokenizer = AutoTokenizer.from_pretrained("GeneZC/bert-large-mrpc") model = BertCls.from_pretrained("GeneZC/bert-large-mrpc") - Notebooks
- Google Colab
- Kaggle
File size: 820 Bytes
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"_name_or_path": "bert-large-mrpc",
"architectures": [
"BertCls"
],
"attention_probs_dropout_prob": 0.1,
"classifier_dropout": null,
"gradient_checkpointing": false,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"hidden_size": 1024,
"initializer_range": 0.02,
"intermediate_size": 4096,
"layer_norm_eps": 1e-12,
"layer_skip": 1,
"max_position_embeddings": 512,
"model_type": "bert",
"num_attention_heads": 16,
"num_hidden_layers": 24,
"num_relation_heads": 32,
"pad_token_id": 0,
"position_embedding_type": "absolute",
"sparsity": "0",
"sparsity_map": {
"0": {
"head": {},
"neuron": {}
}
},
"teacher": false,
"torch_dtype": "float32",
"transformers_version": "4.12.3",
"type_vocab_size": 2,
"use_cache": true,
"vocab_size": 30522
}
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