Sentence Similarity
sentence-transformers
Safetensors
xlm-roberta
feature-extraction
Generated from Trainer
dataset_size:16000
loss:CoSENTLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use Pascalymb/result_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Pascalymb/result_model with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Pascalymb/result_model") sentences = [ "A man and woman are walking in a restaurant that has signs in Chinese.", "A newlywed couple is walking through a Chinese restaurant.", "The woman is sitting on the ground.", "they are playing basketball" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Training in progress, step 2000
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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4.0,2000,0.6451319585193018,0.6628169159235177
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model.safetensors
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oid sha256:1aae9874c9de148c50ee0129f3f129b4dbd291adb19efb905d37cd540bb9bc99
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size 2239607120
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