Feature Extraction
sentence-transformers
PyTorch
Core ML
ONNX
Safetensors
English
bert
sentence-similarity
mteb
custom_code
Eval Results (legacy)
text-embeddings-inference
Instructions to use jinaai/jina-embeddings-v2-small-en with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use jinaai/jina-embeddings-v2-small-en with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("jinaai/jina-embeddings-v2-small-en", 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] - Notebooks
- Google Colab
- Kaggle
Commit ·
b63f8d3
1
Parent(s): 03662f5
Rename implementation repo to `jina-bert-implementation` (#3)
Browse files- Rename implementation repo to `jina-bert-implementation` (e2ed08d90e2593a81c84414cccd6bef79fe5c251)
- config.json +5 -5
config.json
CHANGED
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@@ -1,15 +1,15 @@
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{
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"_name_or_path": "jinaai/jina-
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"model_max_length": 8192,
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"architectures": [
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"JinaBertForMaskedLM"
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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": "jinaai/jina-
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"AutoModelForMaskedLM": "jinaai/jina-
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"AutoModel": "jinaai/jina-
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"AutoModelForSequenceClassification": "jinaai/jina-
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},
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"classifier_dropout": null,
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"gradient_checkpointing": false,
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{
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"_name_or_path": "jinaai/jina-bert-implementation",
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"model_max_length": 8192,
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"architectures": [
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"JinaBertForMaskedLM"
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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": "jinaai/jina-bert-implementation--configuration_bert.JinaBertConfig",
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"AutoModelForMaskedLM": "jinaai/jina-bert-implementation--modeling_bert.JinaBertForMaskedLM",
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"AutoModel": "jinaai/jina-bert-implementation--modeling_bert.JinaBertModel",
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"AutoModelForSequenceClassification": "jinaai/jina-bert-implementation--modeling_bert.JinaBertForSequenceClassification"
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},
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"classifier_dropout": null,
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"gradient_checkpointing": false,
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