Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:664
loss:DenoisingAutoEncoderLoss
text-embeddings-inference
Instructions to use ravch/fine_tuned_bge_small_en_v1.5_another_data_formate with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use ravch/fine_tuned_bge_small_en_v1.5_another_data_formate with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("ravch/fine_tuned_bge_small_en_v1.5_another_data_formate") sentences = [ "of fresh for in for that,, stream_id", "Number of functional/operational toilets for boys with disabilities or CWSN(Children with special needs) ", "Indicates grant for sports and physical education expenditure (in Rs) spent by the school during the financial year 2022-2023 under Samagra Shiksha, corresponding to the udise_sch_code. ", "Number of fresh enrollments for transgenders in class 11 for that school. corresponding to udise_sch_code, caste_id, stream_id. " ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
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