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
File size: 391 Bytes
cdbec7c | 1 2 3 4 5 6 7 8 9 10 11 12 13 | from transformers import AutoConfig
from transformers.models.mistral import MistralConfig
BIDIR_MISTRAL_TYPE = "bidir_mistral"
class BidirectionalMistralConfig(MistralConfig):
model_type = BIDIR_MISTRAL_TYPE
keys_to_ignore_at_inference = ["past_key_values"]
AutoConfig.register(BIDIR_MISTRAL_TYPE, BidirectionalMistralConfig)
BidirectionalMistralConfig.register_for_auto_class() |