Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
dense
Generated from Trainer
dataset_size:11600
loss:MultipleNegativesRankingLoss
text-embeddings-inference
Instructions to use GozdeA/tennis-multi-return-catboost-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use GozdeA/tennis-multi-return-catboost-v2 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("GozdeA/tennis-multi-return-catboost-v2") sentences = [ "Show me contest time", "How did Shelton and he compare in momentum during set 2?", "What is the key factors for Djokovic?", "What is the how many winners for Djokovic?" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Upload all_classifier_configs.json with huggingface_hub
Browse files- all_classifier_configs.json +33 -0
all_classifier_configs.json
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{
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"knn": {
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"method": "knn",
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"k": 16,
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"threshold": 0.05170882480633016,
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"max_candidates": 2,
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"recall": 0.6216666666666666
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},
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"catboost": {
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"method": "catboost",
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"threshold": 0.28170716125509304,
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"recall": 0.6216666666666666,
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"params": {
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"cb_iter": 150,
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"cb_depth": 3,
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"cb_lr": 0.012063744447371417,
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"cb_l2": 8.39301492562091,
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"cb_threshold": 0.28170716125509304
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}
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},
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"mlp": {
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"method": "mlp",
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"threshold": 0.30623464107831183,
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"recall": 0.8033333333333335,
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"params": {
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"mlp_h1": 64,
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"mlp_h2": 32,
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"mlp_lr": 0.0001008326529713479,
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"mlp_alpha": 5.50599590057979e-05,
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"mlp_threshold": 0.30623464107831183
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}
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}
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}
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