Sentence Similarity
sentence-transformers
PyTorch
ONNX
Safetensors
Transformers
Transformers.js
English
nomic_bert
feature-extraction
mteb
custom_code
Eval Results (legacy)
text-embeddings-inference
Instructions to use nomic-ai/nomic-embed-text-v1 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 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("nomic-ai/nomic-embed-text-v1", 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] - Transformers
How to use nomic-ai/nomic-embed-text-v1 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("nomic-ai/nomic-embed-text-v1", trust_remote_code=True) model = AutoModel.from_pretrained("nomic-ai/nomic-embed-text-v1", trust_remote_code=True, device_map="auto") - Transformers.js
How to use nomic-ai/nomic-embed-text-v1 with Transformers.js:
// npm i @huggingface/transformers import { pipeline } from '@huggingface/transformers'; // Allocate pipeline const pipe = await pipeline('sentence-similarity', 'nomic-ai/nomic-embed-text-v1'); - Notebooks
- Google Colab
- Kaggle
change max pos
Browse files- config.json +1 -1
config.json
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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":
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"model_type": "nomic_bert",
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"num_attention_heads": 12,
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"num_hidden_layers": 12,
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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": 8192,
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"model_type": "nomic_bert",
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"num_attention_heads": 12,
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"num_hidden_layers": 12,
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