Sentence Similarity
sentence-transformers
PyTorch
ONNX
nomic_bert
feature-extraction
mteb
custom_code
Eval Results (legacy)
text-embeddings-inference
Instructions to use nomic-ai/nomic-embed-text-v1-ablated with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use nomic-ai/nomic-embed-text-v1-ablated with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("nomic-ai/nomic-embed-text-v1-ablated", trust_remote_code=True) sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Update config.json
Browse files- config.json +3 -3
config.json
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@@ -11,7 +11,7 @@
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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": 0.0,
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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": 0.0,
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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": 0.0,
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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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