Feature Extraction
Transformers
Safetensors
sentence-transformers
English
Hindi
bidir_mistral
sentence-similarity
custom_code
Instructions to use Sailesh97/Hinvec with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sailesh97/Hinvec with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Sailesh97/Hinvec", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Sailesh97/Hinvec", trust_remote_code=True, device_map="auto") - sentence-transformers
How to use Sailesh97/Hinvec with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Sailesh97/Hinvec", 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
Upload config.json with huggingface_hub
Browse files- config.json +4 -0
config.json
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"architectures": [
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"BidirectionalMistralModel"
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],
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"attention_dropout": 0.2,
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"bos_token_id": 6,
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"eos_token_id": 3,
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"architectures": [
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"BidirectionalMistralModel"
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],
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"auto_map": {
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"AutoConfig": "configuration_hinvec.BidirectionalMistralConfig",
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"AutoModel": "modeling_hinvec.BidirectionalMistralModel"
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},
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"attention_dropout": 0.2,
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"bos_token_id": 6,
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"eos_token_id": 3,
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