Feature Extraction
sentence-transformers
ONNX
Transformers
bert
onnxruntime
reranker
int8
int4
text-embeddings-inference
Instructions to use jrc2139/e5-small-v2-ONNX with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use jrc2139/e5-small-v2-ONNX with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("jrc2139/e5-small-v2-ONNX") 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
How to use jrc2139/e5-small-v2-ONNX with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="jrc2139/e5-small-v2-ONNX")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("jrc2139/e5-small-v2-ONNX") model = AutoModel.from_pretrained("jrc2139/e5-small-v2-ONNX", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Upload folder using huggingface_hub
Browse files- 1_Pooling/config.json +2 -7
- README.md +1 -1
- config.json +2 -2
- config_sentence_transformers.json +7 -7
- modules.json +3 -3
- onnx/model.onnx +2 -2
- sentence_bert_config.json +8 -2
1_Pooling/config.json
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{
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"
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"
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"pooling_mode_mean_tokens": true,
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"pooling_mode_max_tokens": false,
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"pooling_mode_mean_sqrt_len_tokens": false,
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"pooling_mode_weightedmean_tokens": false,
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"pooling_mode_lasttoken": false,
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"include_prompt": true
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}
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{
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"embedding_dimension": 384,
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"pooling_mode": "mean",
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"include_prompt": true
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}
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README.md
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@@ -24,7 +24,7 @@ from sentence_transformers import SentenceTransformer
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model = SentenceTransformer(
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"jrc2139/e5-small-v2-ONNX",
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backend="onnx",
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model_kwargs={"file_name": "onnx/
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trust_remote_code=True
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)
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```
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model = SentenceTransformer(
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"jrc2139/e5-small-v2-ONNX",
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backend="onnx",
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model_kwargs={"file_name": "onnx/model_q4.onnx"},
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trust_remote_code=True
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)
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```
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config.json
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],
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"attention_probs_dropout_prob": 0.1,
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"classifier_dropout": null,
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"hidden_act": "gelu",
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"hidden_dropout_prob": 0.1,
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"hidden_size": 384,
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"num_hidden_layers": 12,
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"pad_token_id": 0,
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"position_embedding_type": "absolute",
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"
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"transformers_version": "4.55.4",
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"type_vocab_size": 2,
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"use_cache": true,
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"vocab_size": 30522
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],
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"attention_probs_dropout_prob": 0.1,
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"classifier_dropout": null,
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"dtype": "float32",
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"hidden_act": "gelu",
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"hidden_dropout_prob": 0.1,
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"hidden_size": 384,
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"num_hidden_layers": 12,
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"pad_token_id": 0,
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"position_embedding_type": "absolute",
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"transformers_version": "4.57.6",
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"type_vocab_size": 2,
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"use_cache": true,
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"vocab_size": 30522
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config_sentence_transformers.json
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{
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"model_type": "SentenceTransformer",
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"__version__": {
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"prompts": {
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"default_prompt_name": null,
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"similarity_fn_name": "cosine"
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}
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{
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"__version__": {
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"pytorch": "2.11.0",
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"sentence_transformers": "5.4.1",
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"transformers": "4.57.6"
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},
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"default_prompt_name": null,
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"model_type": "SentenceTransformer",
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"prompts": {
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"document": "",
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"query": ""
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},
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"similarity_fn_name": "cosine"
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}
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modules.json
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"idx": 0,
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"name": "0",
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"path": "",
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"type": "sentence_transformers.
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},
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{
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"idx": 1,
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"name": "1",
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"path": "1_Pooling",
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"type": "sentence_transformers.
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},
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{
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"idx": 2,
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"name": "2",
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"path": "2_Normalize",
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"type": "sentence_transformers.
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}
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]
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"idx": 0,
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"name": "0",
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"path": "",
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"type": "sentence_transformers.base.modules.transformer.Transformer"
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},
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{
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"idx": 1,
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"name": "1",
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"path": "1_Pooling",
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"type": "sentence_transformers.sentence_transformer.modules.pooling.Pooling"
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},
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{
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"idx": 2,
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"name": "2",
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"path": "2_Normalize",
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"type": "sentence_transformers.sentence_transformer.modules.normalize.Normalize"
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}
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]
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onnx/model.onnx
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version https://git-lfs.github.com/spec/v1
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oid sha256:
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size
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version https://git-lfs.github.com/spec/v1
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oid sha256:ed73190ada37b65a103b2188897602bce416e51dadd4c8402bb93252fac23e4f
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size 133041945
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sentence_bert_config.json
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{
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}
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{
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"transformer_task": "feature-extraction",
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"modality_config": {
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"text": {
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"method": "forward",
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"method_output_name": "last_hidden_state"
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}
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},
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"module_output_name": "token_embeddings"
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}
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