Sentence Similarity
sentence-transformers
Safetensors
xlm-roberta
feature-extraction
Generated from Trainer
dataset_size:477170
loss:MultipleNegativesRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use sanganaka/bge-m3-sanskritFT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use sanganaka/bge-m3-sanskritFT with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("sanganaka/bge-m3-sanskritFT") sentences = [ "These bodhisattvas were named", "अमुष्मै त्वा वज्रम् प्रहरामीति यद्यभिचरेद्वज्रो वै स्फ्य स्तृणुते हैवैनेन ॥", "तद्यत्स्रुचः सम्मार्ष्टि यथा वै देवानां चरणं तद्वा अनु मनुष्याणां तस्माद्यदामनुष्याणाम् परिवेषणमुपक्ल्प्तम् भवति ॥", "सुमतिना च । सुजातेन च ।" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
| { | |
| "_name_or_path": "semantic_similarity/BAAI.bge-m3/24-10T02.16/final", | |
| "architectures": [ | |
| "XLMRobertaModel" | |
| ], | |
| "attention_probs_dropout_prob": 0.1, | |
| "bos_token_id": 0, | |
| "classifier_dropout": null, | |
| "eos_token_id": 2, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 1024, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 4096, | |
| "layer_norm_eps": 1e-05, | |
| "max_position_embeddings": 8194, | |
| "model_type": "xlm-roberta", | |
| "num_attention_heads": 16, | |
| "num_hidden_layers": 24, | |
| "output_past": true, | |
| "pad_token_id": 1, | |
| "position_embedding_type": "absolute", | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.42.4", | |
| "type_vocab_size": 1, | |
| "use_cache": true, | |
| "vocab_size": 250002 | |
| } | |