Sentence Similarity
sentence-transformers
ONNX
Safetensors
Transformers.js
gte
feature-extraction
mteb
arctic
snowflake-arctic-embed
custom_code
Eval Results (legacy)
Eval Results
Instructions to use Snowflake/snowflake-arctic-embed-m-v2.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Snowflake/snowflake-arctic-embed-m-v2.0 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Snowflake/snowflake-arctic-embed-m-v2.0", trust_remote_code=True) sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Transformers.js
How to use Snowflake/snowflake-arctic-embed-m-v2.0 with Transformers.js:
// npm i @huggingface/transformers import { pipeline } from '@huggingface/transformers'; // Allocate pipeline const pipe = await pipeline('feature-extraction', 'Snowflake/snowflake-arctic-embed-m-v2.0'); - Notebooks
- Google Colab
- Kaggle
Update config.json
Browse files- config.json +5 -11
config.json
CHANGED
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{
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"_name_or_path": "/data/.model_and_tokenizer_cache/eea825a7e388c292874bd28736f8bcd70ec7526432cd66fa94c540bd27834d22",
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"architectures": [
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"
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],
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"attention_probs_dropout_prob": 0.0,
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"auto_map": {
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"AutoConfig": "
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"AutoModel": "
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"AutoModelForMaskedLM": "Alibaba-NLP/new-impl--modeling.NewForMaskedLM",
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"AutoModelForMultipleChoice": "Alibaba-NLP/new-impl--modeling.NewForMultipleChoice",
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"AutoModelForQuestionAnswering": "Alibaba-NLP/new-impl--modeling.NewForQuestionAnswering",
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"AutoModelForSequenceClassification": "Alibaba-NLP/new-impl--modeling.NewForSequenceClassification",
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"AutoModelForTokenClassification": "Alibaba-NLP/new-impl--modeling.NewForTokenClassification"
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},
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"classifier_dropout": 0.1,
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"hidden_act": "gelu",
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"logn_attention_clip1": false,
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"logn_attention_scale": false,
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"max_position_embeddings": 8192,
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"model_type": "
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"num_attention_heads": 12,
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"num_hidden_layers": 12,
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"pack_qkv": true,
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"unpad_inputs": "true",
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"use_memory_efficient_attention": "true",
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"vocab_size": 250048
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}
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{
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"architectures": [
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"GteModel"
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],
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"attention_probs_dropout_prob": 0.0,
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"auto_map": {
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"AutoConfig": "configuration_hf_alibaba_nlp_gte.GteConfig",
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"AutoModel": "modeling_hf_alibaba_nlp_gte.GteModel"
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},
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"classifier_dropout": 0.1,
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"hidden_act": "gelu",
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"logn_attention_clip1": false,
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"logn_attention_scale": false,
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"max_position_embeddings": 8192,
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"model_type": "gte",
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"num_attention_heads": 12,
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"num_hidden_layers": 12,
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"pack_qkv": true,
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"unpad_inputs": "true",
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"use_memory_efficient_attention": "true",
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"vocab_size": 250048
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}
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