Instructions to use deepset/bert-small-mm_retrieval-passage_encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use deepset/bert-small-mm_retrieval-passage_encoder with Transformers:
# Load model directly from transformers import AutoTokenizer, DPRContextEncoder tokenizer = AutoTokenizer.from_pretrained("deepset/bert-small-mm_retrieval-passage_encoder") model = DPRContextEncoder.from_pretrained("deepset/bert-small-mm_retrieval-passage_encoder") - Notebooks
- Google Colab
- Kaggle
File size: 712 Bytes
c764744 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 | {
"_name_or_path": "deepset/bert-small-mm_retrieval-passage_encoder",
"architectures": [
"DPRContextEncoder"
],
"attention_probs_dropout_prob": 0.1,
"gradient_checkpointing": false,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"hidden_size": 512,
"initializer_range": 0.02,
"intermediate_size": 2048,
"language": "english",
"layer_norm_eps": 1e-12,
"max_position_embeddings": 512,
"model_type": "dpr",
"name": "DPRContextEncoder",
"num_attention_heads": 8,
"num_hidden_layers": 4,
"pad_token_id": 0,
"position_embedding_type": "absolute",
"projection_dim": 0,
"revision": null,
"transformers_version": "4.7.0",
"type_vocab_size": 2,
"vocab_size": 30522
}
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