Sentence Similarity
sentence-transformers
PyTorch
Safetensors
Transformers
new
feature-extraction
mteb
custom_code
Eval Results (legacy)
text-embeddings-inference
Instructions to use NovaSearch/stella_en_400M_v5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use NovaSearch/stella_en_400M_v5 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("NovaSearch/stella_en_400M_v5", 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 NovaSearch/stella_en_400M_v5 with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("NovaSearch/stella_en_400M_v5", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update infinity example (#23)
Browse files- Update infinity example (1ef9639c383c81e07149050df8ffdc65c0c3a800)
- Update README.md (8342a640dce9dc10cb571388ce47e1900d1483eb)
Co-authored-by: Michael <michaelfeil@users.noreply.huggingface.co>
README.md
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# [0.32818374 0.80954516]]
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```
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# FAQ
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Q: The details of training?
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# [0.32818374 0.80954516]]
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```
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### infinity_emb
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Usage via [infinity, MIT Licensed](https://github.com/michaelfeil/infinity).
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```bash
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docker run \
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--gpus all -p "7997":"7997" \
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michaelf34/infinity:0.0.69 \
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v2 --model-id dunzhang/stella_en_400M_v5 --revision "refs/pr/24" --dtype bfloat16 --batch-size 16 --device cuda --engine torch --port 7997 --no-bettertransformer
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```
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# FAQ
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Q: The details of training?
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