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
Tom Aarsen commited on
Commit ·
20e3552
1
Parent(s): 40ebbca
Add return_dict to NomicBertModel forward
Browse filesRequired for Sentence Transformers integration
modeling_hf_nomic_bert.py
CHANGED
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@@ -1069,6 +1069,7 @@ class NomicBertModel(NomicBertPreTrainedModel):
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position_ids=None,
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token_type_ids=None,
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attention_mask=None,
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):
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if token_type_ids is None:
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token_type_ids = torch.zeros_like(input_ids)
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@@ -1080,7 +1081,7 @@ class NomicBertModel(NomicBertPreTrainedModel):
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attention_mask = self.get_extended_attention_mask(attention_mask, input_ids.shape)
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sequence_output = self.encoder(
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hidden_states, attention_mask=attention_mask
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)
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pooled_output = self.pooler(sequence_output) if self.pooler is not None else None
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position_ids=None,
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token_type_ids=None,
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attention_mask=None,
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return_dict=None,
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):
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if token_type_ids is None:
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token_type_ids = torch.zeros_like(input_ids)
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attention_mask = self.get_extended_attention_mask(attention_mask, input_ids.shape)
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sequence_output = self.encoder(
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hidden_states, attention_mask=attention_mask, return_dict=return_dict,
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)
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pooled_output = self.pooler(sequence_output) if self.pooler is not None else None
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