Text Classification
sentence-transformers
Safetensors
English
modernbert
cross-encoder
sts
stsb
stsbenchmark-sts
Eval Results (legacy)
text-embeddings-inference
Instructions to use dleemiller/EttinX-sts-xxs with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use dleemiller/EttinX-sts-xxs with sentence-transformers:
from sentence_transformers import CrossEncoder model = CrossEncoder("dleemiller/EttinX-sts-xxs") query = "Which planet is known as the Red Planet?" passages = [ "Venus is often called Earth's twin because of its similar size and proximity.", "Mars, known for its reddish appearance, is often referred to as the Red Planet.", "Jupiter, the largest planet in our solar system, has a prominent red spot.", "Saturn, famous for its rings, is sometimes mistaken for the Red Planet." ] scores = model.predict([(query, passage) for passage in passages]) print(scores) - Notebooks
- Google Colab
- Kaggle
Upload folder using huggingface_hub
Browse files
config.json
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"causal_mask": false,
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"classifier_activation": "gelu",
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"classifier_bias": false,
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"classifier_dropout": 0.
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"classifier_pooling": "mean",
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"cls_token_id": 50281,
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"decoder_bias": true,
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"causal_mask": false,
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"classifier_activation": "gelu",
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"classifier_bias": false,
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"classifier_dropout": 0.1,
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"classifier_pooling": "mean",
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"cls_token_id": 50281,
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"decoder_bias": true,
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eval/CrossEncoderCorrelationEvaluator_sts-test-eval_results.csv
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epoch,steps,Pearson_Correlation,Spearman_Correlation
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-1,-1,0.8413715686076841,0.8310895302151975
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eval/CrossEncoderCorrelationEvaluator_sts-validation-eval_results.csv
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epoch,steps,Pearson_Correlation,Spearman_Correlation
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-1,-1,0.8815197312565873,0.8786002071426082
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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size 67458708
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version https://git-lfs.github.com/spec/v1
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size 67458708
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