Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:45
loss:MultipleNegativesRankingLoss
text-embeddings-inference
Instructions to use embedingHF/fine_tuned_bilingual_model_v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use embedingHF/fine_tuned_bilingual_model_v2 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("embedingHF/fine_tuned_bilingual_model_v2") sentences = [ "map location", "is garage available", "how much is it", "bedrooms kitnay hain" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
File size: 588 Bytes
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"is_local": false,
"mask_token": "[MASK]",
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"model_max_length": 256,
"never_split": null,
"pad_to_multiple_of": null,
"pad_token": "[PAD]",
"pad_token_type_id": 0,
"padding_side": "right",
"sep_token": "[SEP]",
"stride": 0,
"strip_accents": null,
"tokenize_chinese_chars": true,
"tokenizer_class": "BertTokenizer",
"truncation_side": "right",
"truncation_strategy": "longest_first",
"unk_token": "[UNK]"
}
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