Feature Extraction
Transformers
Safetensors
sentence-transformers
bharatmorph_embedding
embeddings
multilingual
indic-languages
cross-lingual-retrieval
phoneme-aware
morpheme-aware
semantic-similarity
custom_code
Instructions to use Girinath11/bharatmorph_indic_crosslingual with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Girinath11/bharatmorph_indic_crosslingual with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Girinath11/bharatmorph_indic_crosslingual", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Girinath11/bharatmorph_indic_crosslingual", trust_remote_code=True, device_map="auto") - sentence-transformers
How to use Girinath11/bharatmorph_indic_crosslingual with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Girinath11/bharatmorph_indic_crosslingual", 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
Update config.json
Browse files- config.json +0 -1
config.json
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"morph_dropout": 0.1,
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"num_langs": 6,
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"num_morph_types": 8,
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"pad_token_id": 2,
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"proj_dim": 256,
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"sandhi_k": 7,
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"transformers_version": "4.40.0",
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"morph_dropout": 0.1,
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"num_langs": 6,
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"num_morph_types": 8,
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"proj_dim": 256,
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"sandhi_k": 7,
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"transformers_version": "4.40.0",
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