CrossEncoder based on NAMAA-Space/GATE-Reranker-V1

This is a Cross Encoder model finetuned from NAMAA-Space/GATE-Reranker-V1 using the sentence-transformers library. It computes scores for pairs of texts, which can be used for text reranking and semantic search.

Model Details

Model Description

Model Sources

Usage

Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

pip install -U sentence-transformers

Then you can load this model and run inference.

from sentence_transformers import CrossEncoder

# Download from the ๐Ÿค— Hub
model = CrossEncoder("yoriis/GTE-quqa-haqa")
# Get scores for pairs of texts
pairs = [
    ['ู‡ู„  ุชุฑูƒ ุงู„ุตู„ุงุฉ ุชู‡ุงูˆู†ุง ูˆูƒุณู„ุง ูƒุจูŠุฑุฉ ู…ู† ุงู„ูƒุจุงุฆุฑุŒ ูˆู…ู† ุงู„ุนู„ู…ุงุก ู…ู† ู‚ุงู„ ุจูƒูุฑู‡ุŒ ู‡ู„ ู‡ุฐุง ุงู„ุญูƒู… ู„ู‡ ุชูˆุฌูŠู‡ู‡ ู…ู† ุงู„ุณู†ุฉ ุงู„ู†ุจูˆูŠุฉุŸ', 'ุญุฏูŠุซ ุทูŽุงุฑูู‚ู ุจู’ู†ู ุดูู‡ูŽุงุจู ุฑุถูŠ ุงู„ู„ู‡ ุนู†ู‡ุŒ ุนูŽู†ู ุงู„ู†ู‘ูŽุจููŠู‘ู ๏ทบ ู‚ูŽุงู„ูŽ: ยซุงู„ุฌูู…ูุนูŽุฉู ุญูŽู‚ู‘ูŒ ูˆูŽุงุฌูุจูŒ ุนูŽู„ูŽู‰ ูƒูู„ู‘ู ู…ูุณู’ู„ูู…ู ูููŠ ุฌูŽู…ูŽุงุนูŽุฉู ุฅูู„ู‘ูŽุง ุฃูŽุฑู’ุจูŽุนูŽุฉู‹: ุนูŽุจู’ุฏูŒ ู…ูŽู…ู’ู„ููˆูƒูŒุŒ ุฃูŽูˆู ุงู…ู’ุฑูŽุฃูŽุฉูŒุŒ ุฃูŽูˆู’ ุตูŽุจููŠู‘ูŒุŒ ุฃูŽูˆู’ ู…ูŽุฑููŠุถูŒยป. ุฑูˆุงู‡ ุฃุจูˆ ุฏุงูˆุฏ (1067)ุŒ ูˆุตุญุญู‡ ุงู„ุฃู„ุจุงู†ูŠ ููŠ ุฅุฑูˆุงุก ุงู„ุบู„ูŠู„ (592)ุŒ ูˆุงู„ูˆุงุฏุนูŠ ููŠ ุงู„ุตุญูŠุญ ุงู„ู…ุณู†ุฏ (517) .'],
    ['ู…ู† ู‡ูˆ ุงู„ู†ุจูŠ ุงู„ุฐูŠ ูƒุงู† ูŠุนู…ู„ ู†ุฌุงุฑุง ุŸ', 'ุนู† ุฃุจูŠ ุจู† ูƒุนุจ ุฑุถูŠ ุงู„ู„ู‡ ุนู†ู‡ ู‚ุงู„: ยซุฅู† ุฑุณูˆู„ ุงู„ู„ู‡ ๏ทบ ูƒุงู† ูŠูˆุชุฑ ููŠู‚ู†ุช ู‚ุจู„ ุงู„ุฑูƒูˆุนยป. ุฃุฎุฑุฌู‡ ุงุจู† ู…ุงุฌู‡.'],
    ['ู…ุง ุณุจุจ ูƒุฑุงู‡ูŠุฉ ุงู„ุตู„ุงุฉ ุนู„ู‰ ุงู„ุณุฌู‘ุงุฏ ุงู„ู…ุฒุฎุฑูุŸ', 'ุงุจู† ุนุจุงุณ ุฑุถูŠ ุงู„ู„ู‡ ุนู†ู‡ ุนู† ุงู„ู†ุจูŠ ๏ทบ ุฃู†ู‡ ู‚ุงู„: (ู…ู† ุณู…ุน ุงู„ู†ุฏุงุก ูู„ู… ูŠุฃุชู‡ุŒ ูู„ุง ุตู„ุงุฉ ู„ู‡ ุฅู„ุง ู…ู† ุนุฐุฑ). ุฃุฎุฑุฌู‡ ุงุจู† ู…ุงุฌู‡'],
    ['ู…ู† ู‡ูˆ ุงู„ุตุญุงุจูŠ ุงู„ุฐูŠ ู‚ุงู„ ููŠู‡ ุงู„ู†ุจูŠ ๏ทบ: ยซู…ู† ุฎูŠุฑ ุฐูŠ ูŠู…ู† ูˆุนู„ู‰ ูˆุฌู‡ู‡ ู…ุณุญุฉ ู…ู„ูƒยป ุŸ', 'ุญุฏูŠุซ  ุฌูŽุฑููŠุฑ ุจู’ู† ุนูŽุจู’ุฏู ุงู„ู„ู‡ ุงู„ุจูŽุฌูŽู„ููŠู‘ูŽ ุฑุถูŠ ุงู„ู„ู‡ ุนู†ู‡ุŒ ู…ูŽุง ุฑูŽุขู†ููŠ ุฑูŽุณููˆู„ู ุงู„ู„ู‡ ๏ทบ ู‚ูŽุทู‘ู ุฅูู„ู‘ูŽุง ุชูŽุจูŽุณู‘ูŽู…ูŽ ูููŠ ูˆูŽุฌู’ู‡ููŠ ู‚ูŽุงู„ูŽ: ูˆูŽู‚ูŽุงู„ูŽ ุฑูŽุณููˆู„ู ุงู„ู„ู‡ ๏ทบ: ยซูŠูŽุทู’ู„ูุนู ุนูŽู„ูŽูŠู’ูƒูู…ู’ ู…ูู†ู’ ู‡ูŽุฐูŽุง ุงู„ุจูŽุงุจู ุฑูŽุฌูู„ูŒ ู…ูู†ู’ ุฎูŽูŠู’ุฑู ุฐููŠ ูŠูู…ู’ู†ูุŒ ุนูŽู„ูŽู‰ ูˆูŽุฌู’ู‡ูู‡ู ู…ูุณู’ุญูŽุฉู ู…ูŽู„ูŽูƒูุŒ ููŽุทูŽู„ูŽุนูŽ ุฌูŽุฑููŠุฑู ุจู’ู†ู ุนูŽุจู’ุฏู ุงู„ู„ู‡ยป. ูˆู‡ูˆ ููŠ ู…ุณู†ุฏ ุงู„ุฅู…ุงู… ุฃุญู…ุฏ (19179)ุŒ ูˆู‡ูˆ ููŠ ุงู„ุตุญูŠุญุฉ (3193)ุŒ ูˆููŠ ุงู„ุตุญูŠุญ ุงู„ู…ุณู†ุฏ (262).'],
    ['ู…ุง ูุถู„ ุตู„ุงุฉ ุงู„ู„ูŠู„ุŸ', 'ุนูŽู†ู’ ุฃูŽุจููŠ ู‡ูุฑูŽูŠู’ุฑูŽุฉูŽ ุฑุถูŠ ุงู„ู„ู‡ ุนู†ู‡ุŒ ุฃูŽู†ู‘ูŽ ุฑูŽุณููˆู„ูŽ ุงู„ู„ู‡ ๏ทบ ู‚ูŽุงู„ูŽ: ยซู„ูŽูŠู’ุณูŽ ุงู„ุดู‘ูŽุฏููŠุฏู ุจูุงู„ุตู‘ูุฑูŽุนูŽุฉู ุฅูู†ู‘ูŽู…ูŽุง ุงู„ุดู‘ูŽุฏููŠุฏู ุงู„ู‘ูŽุฐููŠ ูŠูŽู…ู’ู„ููƒู ู†ูŽูู’ุณูŽู‡ู ุนูู†ู’ุฏูŽ ุงู„ุบูŽุถูŽุจูยป. ุฑูˆุงู‡ ุงู„ุจุฎุงุฑูŠ (6114)ุŒ ูˆู…ุณู„ู… (2609).'],
]
scores = model.predict(pairs)
print(scores.shape)
# (5,)

# Or rank different texts based on similarity to a single text
ranks = model.rank(
    'ู‡ู„  ุชุฑูƒ ุงู„ุตู„ุงุฉ ุชู‡ุงูˆู†ุง ูˆูƒุณู„ุง ูƒุจูŠุฑุฉ ู…ู† ุงู„ูƒุจุงุฆุฑุŒ ูˆู…ู† ุงู„ุนู„ู…ุงุก ู…ู† ู‚ุงู„ ุจูƒูุฑู‡ุŒ ู‡ู„ ู‡ุฐุง ุงู„ุญูƒู… ู„ู‡ ุชูˆุฌูŠู‡ู‡ ู…ู† ุงู„ุณู†ุฉ ุงู„ู†ุจูˆูŠุฉุŸ',
    [
        'ุญุฏูŠุซ ุทูŽุงุฑูู‚ู ุจู’ู†ู ุดูู‡ูŽุงุจู ุฑุถูŠ ุงู„ู„ู‡ ุนู†ู‡ุŒ ุนูŽู†ู ุงู„ู†ู‘ูŽุจููŠู‘ู ๏ทบ ู‚ูŽุงู„ูŽ: ยซุงู„ุฌูู…ูุนูŽุฉู ุญูŽู‚ู‘ูŒ ูˆูŽุงุฌูุจูŒ ุนูŽู„ูŽู‰ ูƒูู„ู‘ู ู…ูุณู’ู„ูู…ู ูููŠ ุฌูŽู…ูŽุงุนูŽุฉู ุฅูู„ู‘ูŽุง ุฃูŽุฑู’ุจูŽุนูŽุฉู‹: ุนูŽุจู’ุฏูŒ ู…ูŽู…ู’ู„ููˆูƒูŒุŒ ุฃูŽูˆู ุงู…ู’ุฑูŽุฃูŽุฉูŒุŒ ุฃูŽูˆู’ ุตูŽุจููŠู‘ูŒุŒ ุฃูŽูˆู’ ู…ูŽุฑููŠุถูŒยป. ุฑูˆุงู‡ ุฃุจูˆ ุฏุงูˆุฏ (1067)ุŒ ูˆุตุญุญู‡ ุงู„ุฃู„ุจุงู†ูŠ ููŠ ุฅุฑูˆุงุก ุงู„ุบู„ูŠู„ (592)ุŒ ูˆุงู„ูˆุงุฏุนูŠ ููŠ ุงู„ุตุญูŠุญ ุงู„ู…ุณู†ุฏ (517) .',
        'ุนู† ุฃุจูŠ ุจู† ูƒุนุจ ุฑุถูŠ ุงู„ู„ู‡ ุนู†ู‡ ู‚ุงู„: ยซุฅู† ุฑุณูˆู„ ุงู„ู„ู‡ ๏ทบ ูƒุงู† ูŠูˆุชุฑ ููŠู‚ู†ุช ู‚ุจู„ ุงู„ุฑูƒูˆุนยป. ุฃุฎุฑุฌู‡ ุงุจู† ู…ุงุฌู‡.',
        'ุงุจู† ุนุจุงุณ ุฑุถูŠ ุงู„ู„ู‡ ุนู†ู‡ ุนู† ุงู„ู†ุจูŠ ๏ทบ ุฃู†ู‡ ู‚ุงู„: (ู…ู† ุณู…ุน ุงู„ู†ุฏุงุก ูู„ู… ูŠุฃุชู‡ุŒ ูู„ุง ุตู„ุงุฉ ู„ู‡ ุฅู„ุง ู…ู† ุนุฐุฑ). ุฃุฎุฑุฌู‡ ุงุจู† ู…ุงุฌู‡',
        'ุญุฏูŠุซ  ุฌูŽุฑููŠุฑ ุจู’ู† ุนูŽุจู’ุฏู ุงู„ู„ู‡ ุงู„ุจูŽุฌูŽู„ููŠู‘ูŽ ุฑุถูŠ ุงู„ู„ู‡ ุนู†ู‡ุŒ ู…ูŽุง ุฑูŽุขู†ููŠ ุฑูŽุณููˆู„ู ุงู„ู„ู‡ ๏ทบ ู‚ูŽุทู‘ู ุฅูู„ู‘ูŽุง ุชูŽุจูŽุณู‘ูŽู…ูŽ ูููŠ ูˆูŽุฌู’ู‡ููŠ ู‚ูŽุงู„ูŽ: ูˆูŽู‚ูŽุงู„ูŽ ุฑูŽุณููˆู„ู ุงู„ู„ู‡ ๏ทบ: ยซูŠูŽุทู’ู„ูุนู ุนูŽู„ูŽูŠู’ูƒูู…ู’ ู…ูู†ู’ ู‡ูŽุฐูŽุง ุงู„ุจูŽุงุจู ุฑูŽุฌูู„ูŒ ู…ูู†ู’ ุฎูŽูŠู’ุฑู ุฐููŠ ูŠูู…ู’ู†ูุŒ ุนูŽู„ูŽู‰ ูˆูŽุฌู’ู‡ูู‡ู ู…ูุณู’ุญูŽุฉู ู…ูŽู„ูŽูƒูุŒ ููŽุทูŽู„ูŽุนูŽ ุฌูŽุฑููŠุฑู ุจู’ู†ู ุนูŽุจู’ุฏู ุงู„ู„ู‡ยป. ูˆู‡ูˆ ููŠ ู…ุณู†ุฏ ุงู„ุฅู…ุงู… ุฃุญู…ุฏ (19179)ุŒ ูˆู‡ูˆ ููŠ ุงู„ุตุญูŠุญุฉ (3193)ุŒ ูˆููŠ ุงู„ุตุญูŠุญ ุงู„ู…ุณู†ุฏ (262).',
        'ุนูŽู†ู’ ุฃูŽุจููŠ ู‡ูุฑูŽูŠู’ุฑูŽุฉูŽ ุฑุถูŠ ุงู„ู„ู‡ ุนู†ู‡ุŒ ุฃูŽู†ู‘ูŽ ุฑูŽุณููˆู„ูŽ ุงู„ู„ู‡ ๏ทบ ู‚ูŽุงู„ูŽ: ยซู„ูŽูŠู’ุณูŽ ุงู„ุดู‘ูŽุฏููŠุฏู ุจูุงู„ุตู‘ูุฑูŽุนูŽุฉู ุฅูู†ู‘ูŽู…ูŽุง ุงู„ุดู‘ูŽุฏููŠุฏู ุงู„ู‘ูŽุฐููŠ ูŠูŽู…ู’ู„ููƒู ู†ูŽูู’ุณูŽู‡ู ุนูู†ู’ุฏูŽ ุงู„ุบูŽุถูŽุจูยป. ุฑูˆุงู‡ ุงู„ุจุฎุงุฑูŠ (6114)ุŒ ูˆู…ุณู„ู… (2609).',
    ]
)
# [{'corpus_id': ..., 'score': ...}, {'corpus_id': ..., 'score': ...}, ...]

Evaluation

Metrics

Cross Encoder Classification

Metric Value
accuracy 0.9347
accuracy_threshold 0.5419
f1 0.8599
f1_threshold 0.5419
precision 0.9278
recall 0.8012
average_precision 0.9188

Cross Encoder Classification

Metric Value
accuracy 0.8665
accuracy_threshold 0.602
f1 0.4424
f1_threshold 0.113
precision 0.4294
recall 0.4562
average_precision 0.4908

Training Details

Training Dataset

Unnamed Dataset

  • Size: 8,623 training samples
  • Columns: sentence_0, sentence_1, and label
  • Approximate statistics based on the first 1000 samples:
    sentence_0 sentence_1 label
    type string string float
    details
    • min: 9 characters
    • mean: 35.96 characters
    • max: 132 characters
    • min: 39 characters
    • mean: 286.62 characters
    • max: 12356 characters
    • min: 0.0
    • mean: 0.16
    • max: 1.0
  • Samples:
    sentence_0 sentence_1 label
    ู‡ู„ ุชุฑูƒ ุงู„ุตู„ุงุฉ ุชู‡ุงูˆู†ุง ูˆูƒุณู„ุง ูƒุจูŠุฑุฉ ู…ู† ุงู„ูƒุจุงุฆุฑุŒ ูˆู…ู† ุงู„ุนู„ู…ุงุก ู…ู† ู‚ุงู„ ุจูƒูุฑู‡ุŒ ู‡ู„ ู‡ุฐุง ุงู„ุญูƒู… ู„ู‡ ุชูˆุฌูŠู‡ู‡ ู…ู† ุงู„ุณู†ุฉ ุงู„ู†ุจูˆูŠุฉุŸ ุญุฏูŠุซ ุทูŽุงุฑูู‚ู ุจู’ู†ู ุดูู‡ูŽุงุจู ุฑุถูŠ ุงู„ู„ู‡ ุนู†ู‡ุŒ ุนูŽู†ู ุงู„ู†ู‘ูŽุจููŠู‘ู ๏ทบ ู‚ูŽุงู„ูŽ: ยซุงู„ุฌูู…ูุนูŽุฉู ุญูŽู‚ู‘ูŒ ูˆูŽุงุฌูุจูŒ ุนูŽู„ูŽู‰ ูƒูู„ู‘ู ู…ูุณู’ู„ูู…ู ูููŠ ุฌูŽู…ูŽุงุนูŽุฉู ุฅูู„ู‘ูŽุง ุฃูŽุฑู’ุจูŽุนูŽุฉู‹: ุนูŽุจู’ุฏูŒ ู…ูŽู…ู’ู„ููˆูƒูŒุŒ ุฃูŽูˆู ุงู…ู’ุฑูŽุฃูŽุฉูŒุŒ ุฃูŽูˆู’ ุตูŽุจููŠู‘ูŒุŒ ุฃูŽูˆู’ ู…ูŽุฑููŠุถูŒยป. ุฑูˆุงู‡ ุฃุจูˆ ุฏุงูˆุฏ (1067)ุŒ ูˆุตุญุญู‡ ุงู„ุฃู„ุจุงู†ูŠ ููŠ ุฅุฑูˆุงุก ุงู„ุบู„ูŠู„ (592)ุŒ ูˆุงู„ูˆุงุฏุนูŠ ููŠ ุงู„ุตุญูŠุญ ุงู„ู…ุณู†ุฏ (517) . 0.0
    ู…ู† ู‡ูˆ ุงู„ู†ุจูŠ ุงู„ุฐูŠ ูƒุงู† ูŠุนู…ู„ ู†ุฌุงุฑุง ุŸ ุนู† ุฃุจูŠ ุจู† ูƒุนุจ ุฑุถูŠ ุงู„ู„ู‡ ุนู†ู‡ ู‚ุงู„: ยซุฅู† ุฑุณูˆู„ ุงู„ู„ู‡ ๏ทบ ูƒุงู† ูŠูˆุชุฑ ููŠู‚ู†ุช ู‚ุจู„ ุงู„ุฑูƒูˆุนยป. ุฃุฎุฑุฌู‡ ุงุจู† ู…ุงุฌู‡. 0.0
    ู…ุง ุณุจุจ ูƒุฑุงู‡ูŠุฉ ุงู„ุตู„ุงุฉ ุนู„ู‰ ุงู„ุณุฌู‘ุงุฏ ุงู„ู…ุฒุฎุฑูุŸ ุงุจู† ุนุจุงุณ ุฑุถูŠ ุงู„ู„ู‡ ุนู†ู‡ ุนู† ุงู„ู†ุจูŠ ๏ทบ ุฃู†ู‡ ู‚ุงู„: (ู…ู† ุณู…ุน ุงู„ู†ุฏุงุก ูู„ู… ูŠุฃุชู‡ุŒ ูู„ุง ุตู„ุงุฉ ู„ู‡ ุฅู„ุง ู…ู† ุนุฐุฑ). ุฃุฎุฑุฌู‡ ุงุจู† ู…ุงุฌู‡ 0.0
  • Loss: BinaryCrossEntropyLoss with these parameters:
    {
        "activation_fn": "torch.nn.modules.linear.Identity",
        "pos_weight": null
    }
    

Training Hyperparameters

Non-Default Hyperparameters

  • eval_strategy: steps
  • num_train_epochs: 4
  • fp16: True

All Hyperparameters

Click to expand
  • overwrite_output_dir: False
  • do_predict: False
  • eval_strategy: steps
  • prediction_loss_only: True
  • per_device_train_batch_size: 8
  • per_device_eval_batch_size: 8
  • per_gpu_train_batch_size: None
  • per_gpu_eval_batch_size: None
  • gradient_accumulation_steps: 1
  • eval_accumulation_steps: None
  • torch_empty_cache_steps: None
  • learning_rate: 5e-05
  • weight_decay: 0.0
  • adam_beta1: 0.9
  • adam_beta2: 0.999
  • adam_epsilon: 1e-08
  • max_grad_norm: 1
  • num_train_epochs: 4
  • max_steps: -1
  • lr_scheduler_type: linear
  • lr_scheduler_kwargs: {}
  • warmup_ratio: 0.0
  • warmup_steps: 0
  • log_level: passive
  • log_level_replica: warning
  • log_on_each_node: True
  • logging_nan_inf_filter: True
  • save_safetensors: True
  • save_on_each_node: False
  • save_only_model: False
  • restore_callback_states_from_checkpoint: False
  • no_cuda: False
  • use_cpu: False
  • use_mps_device: False
  • seed: 42
  • data_seed: None
  • jit_mode_eval: False
  • use_ipex: False
  • bf16: False
  • fp16: True
  • fp16_opt_level: O1
  • half_precision_backend: auto
  • bf16_full_eval: False
  • fp16_full_eval: False
  • tf32: None
  • local_rank: 0
  • ddp_backend: None
  • tpu_num_cores: None
  • tpu_metrics_debug: False
  • debug: []
  • dataloader_drop_last: False
  • dataloader_num_workers: 0
  • dataloader_prefetch_factor: None
  • past_index: -1
  • disable_tqdm: False
  • remove_unused_columns: True
  • label_names: None
  • load_best_model_at_end: False
  • ignore_data_skip: False
  • fsdp: []
  • fsdp_min_num_params: 0
  • fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
  • fsdp_transformer_layer_cls_to_wrap: None
  • accelerator_config: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
  • deepspeed: None
  • label_smoothing_factor: 0.0
  • optim: adamw_torch
  • optim_args: None
  • adafactor: False
  • group_by_length: False
  • length_column_name: length
  • ddp_find_unused_parameters: None
  • ddp_bucket_cap_mb: None
  • ddp_broadcast_buffers: False
  • dataloader_pin_memory: True
  • dataloader_persistent_workers: False
  • skip_memory_metrics: True
  • use_legacy_prediction_loop: False
  • push_to_hub: False
  • resume_from_checkpoint: None
  • hub_model_id: None
  • hub_strategy: every_save
  • hub_private_repo: None
  • hub_always_push: False
  • hub_revision: None
  • gradient_checkpointing: False
  • gradient_checkpointing_kwargs: None
  • include_inputs_for_metrics: False
  • include_for_metrics: []
  • eval_do_concat_batches: True
  • fp16_backend: auto
  • push_to_hub_model_id: None
  • push_to_hub_organization: None
  • mp_parameters:
  • auto_find_batch_size: False
  • full_determinism: False
  • torchdynamo: None
  • ray_scope: last
  • ddp_timeout: 1800
  • torch_compile: False
  • torch_compile_backend: None
  • torch_compile_mode: None
  • include_tokens_per_second: False
  • include_num_input_tokens_seen: False
  • neftune_noise_alpha: None
  • optim_target_modules: None
  • batch_eval_metrics: False
  • eval_on_start: False
  • use_liger_kernel: False
  • liger_kernel_config: None
  • eval_use_gather_object: False
  • average_tokens_across_devices: False
  • prompts: None
  • batch_sampler: batch_sampler
  • multi_dataset_batch_sampler: proportional
  • router_mapping: {}
  • learning_rate_mapping: {}

Training Logs

Epoch Step Training Loss eval_average_precision
0.3298 500 0.4083 0.8871
0.6596 1000 0.2958 0.9043
0.9894 1500 0.2839 0.9092
1.0 1516 - 0.9091
1.3193 2000 0.2698 0.9129
1.6491 2500 0.2617 0.9152
1.9789 3000 0.2791 0.9163
2.0 3032 - 0.9160
2.3087 3500 0.2651 0.9159
2.6385 4000 0.2475 0.9172
2.9683 4500 0.264 0.9186
3.0 4548 - 0.9187
3.2982 5000 0.225 0.9180
3.6280 5500 0.2706 0.9186
3.9578 6000 0.2242 0.9188
4.0 6064 - 0.9188
0.4638 500 0.5074 0.4693
0.9276 1000 0.3909 0.4817
1.0 1078 - 0.4858
1.3915 1500 0.3806 0.4802
1.8553 2000 0.3638 0.4828
2.0 2156 - 0.4843
2.3191 2500 0.395 0.4828
2.7829 3000 0.347 0.4840
3.0 3234 - 0.4850
3.2468 3500 0.3614 0.4866
3.7106 4000 0.3483 0.4906
4.0 4312 - 0.4908

Framework Versions

  • Python: 3.11.13
  • Sentence Transformers: 5.0.0
  • Transformers: 4.55.0
  • PyTorch: 2.6.0+cu124
  • Accelerate: 1.9.0
  • Datasets: 4.0.0
  • Tokenizers: 0.21.4

Citation

BibTeX

Sentence Transformers

@inproceedings{reimers-2019-sentence-bert,
    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
    author = "Reimers, Nils and Gurevych, Iryna",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
    month = "11",
    year = "2019",
    publisher = "Association for Computational Linguistics",
    url = "https://arxiv.org/abs/1908.10084",
}
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