Sentence Similarity
sentence-transformers
Safetensors
xlm-roberta
feature-extraction
Generated from Trainer
dataset_size:240
loss:CoSENTLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use Ismailea04/result_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Ismailea04/result_model with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Ismailea04/result_model") sentences = [ "A woman is walking across the street eating a banana, while a man is following with his briefcase.", "The bicyclists are dead.", "A man has facial hair.", "the woman is outside" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Training in progress, step 20
Browse files
eval/similarity_evaluation_pair-score-evaluator-dev_results.csv
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epoch,steps,cosine_pearson,cosine_spearman
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0.3333333333333333,10,-0.044685044768923037,-0.019929827364018077
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1.0,30,-0.12068451525682179,-0.0998270817072691
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model.safetensors
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