Merge branch 'main' of into integrations/sentence_transformers
Browse files- README.md +5 -2
- config.json +3 -3
README.md
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## Usage
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### Sentence Transformers
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```python
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from sentence_transformers import SentenceTransformer
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model = SentenceTransformer("nomic-ai/nomic-embed-text-v1-unsupervised", trust_remote_code=True)
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sentences = ['What is TSNE?', 'Who is Laurens van der Maaten?']
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embeddings = model.encode(sentences)
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print(embeddings)
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```
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input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
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return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
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sentences = ['What is TSNE?', 'Who is Laurens van der Maaten?']
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tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')
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model = AutoModel.from_pretrained('nomic-ai/nomic-embed-text-v1-unsupervised', trust_remote_code=True)
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## Usage
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Note `nomic-embed-text` requires prefixes! We support the prefixes `[search_query, search_document, classification, clustering]`.
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For retrieval applications, you should prepend `search_document` for all your documents and `search_query` for your queries.
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### Sentence Transformers
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```python
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from sentence_transformers import SentenceTransformer
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model = SentenceTransformer("nomic-ai/nomic-embed-text-v1-unsupervised", trust_remote_code=True)
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sentences = ['search_query: What is TSNE?', 'search_query: Who is Laurens van der Maaten?']
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embeddings = model.encode(sentences)
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print(embeddings)
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```
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input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
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return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
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sentences = ['search_query: What is TSNE?', 'search_query: Who is Laurens van der Maaten?']
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tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')
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model = AutoModel.from_pretrained('nomic-ai/nomic-embed-text-v1-unsupervised', trust_remote_code=True)
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config.json
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"bos_token_id": null,
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"causal": false,
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"dense_seq_output": true,
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"embd_pdrop": 0.
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"eos_token_id": null,
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"fused_bias_fc": true,
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"fused_dropout_add_ln": true,
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"prenorm": false,
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"qkv_proj_bias": false,
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"reorder_and_upcast_attn": false,
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"resid_pdrop": 0.
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"rotary_emb_base": 1000,
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"rotary_emb_fraction": 1.0,
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"rotary_emb_interleaved": false,
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"scale_attn_by_inverse_layer_idx": false,
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"scale_attn_weights": true,
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"summary_activation": null,
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"summary_first_dropout": 0.
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"summary_proj_to_labels": true,
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"summary_type": "cls_index",
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"summary_use_proj": true,
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"bos_token_id": null,
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"causal": false,
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"dense_seq_output": true,
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"embd_pdrop": 0.1,
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"eos_token_id": null,
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"fused_bias_fc": true,
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"fused_dropout_add_ln": true,
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"prenorm": false,
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"qkv_proj_bias": false,
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"reorder_and_upcast_attn": false,
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"resid_pdrop": 0.1,
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"rotary_emb_base": 1000,
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"rotary_emb_fraction": 1.0,
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"rotary_emb_interleaved": false,
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"scale_attn_by_inverse_layer_idx": false,
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"scale_attn_weights": true,
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"summary_activation": null,
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"summary_first_dropout": 0.1,
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"summary_proj_to_labels": true,
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"summary_type": "cls_index",
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"summary_use_proj": true,
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