CrossEncoder based on yoriis/ce-tydi

This is a Cross Encoder model finetuned from yoriis/ce-tydi 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 Type: Cross Encoder
  • Base model: yoriis/ce-tydi
  • Maximum Sequence Length: 512 tokens
  • Number of Output Labels: 1 label

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/ce-tydi-quqa-haqa")
# Get scores for pairs of texts
pairs = [
    ['ู…ุง ุงู„ุฏุนุงุก ุงู„ูˆุงุฑุฏ ุนู†ุฏ ุงู„ุฏุฎูˆู„ ูˆุงู„ุฎุฑูˆุฌ ู…ู† ุงู„ู…ุณุฌุฏุŸ', 'ุญุฏูŠุซ ุนูŽู†ู’ ุนูู…ูŽุฑูŽ ุจู’ู†ู ุงู„ุฎูŽุทู‘ูŽุงุจู ุฑุถูŠ ุงู„ู„ู‡ ุนู†ู‡ุŒ ู‚ูŽุงู„ูŽ: ู‚ูŽุงู„ูŽ ุฑูŽุณููˆู„ู ุงู„ู„ู‡ ๏ทบ: ยซู…ูŽุง ู…ูู†ู’ูƒูู…ู’ ู…ูู†ู’ ุฃูŽุญูŽุฏู ูŠูŽุชูŽูˆูŽุถู‘ูŽุฃู ููŽูŠูุจู’ู„ูุบู - ุฃูŽูˆู’ ููŽูŠูุณู’ุจูุบู - ุงู„ูˆูŽุถููˆุกูŽ ุซูู…ู‘ูŽ ูŠูŽู‚ููˆู„ู: ุฃูŽุดู’ู‡ูŽุฏู ุฃูŽู†ู’ ู„ูŽุง ุฅูู„ูŽู‡ูŽ ุฅูู„ู‘ูŽุง ุงู„ู„ู‡ ูˆูŽุฃูŽู†ู‘ูŽ ู…ูุญูŽู…ู‘ูŽุฏู‹ุง ุนูŽุจู’ุฏู ุงู„ู„ู‡ ูˆูŽุฑูŽุณููˆู„ูู‡ู ุฅูู„ู‘ูŽุง ููุชูุญูŽุชู’ ู„ูŽู‡ู ุฃูŽุจู’ูˆูŽุงุจู ุงู„ุฌูŽู†ู‘ูŽุฉู ุงู„ุซู‘ูŽู…ูŽุงู†ููŠูŽุฉู ูŠูŽุฏู’ุฎูู„ู ู…ูู†ู’ ุฃูŽูŠู‘ูู‡ูŽุง ุดูŽุงุกูŽยป. ุฑูˆุงู‡ ู…ุณู„ู… (234).'],
    ['ู…ุง ุญูƒู… ู…ู† ู„ู… ูŠู‚ุฑุฃ ุจูุงุชุญุฉ ุงู„ูƒุชุงุจ ุŸ', 'ุญุฏูŠุซ ุฃุจูŠ ุฃู…ุงู…ุฉ ุฑุถูŠ ุงู„ู„ู‡ ุนู†ู‡ ู‚ุงู„: ู‚ุงู„ ุฑุณูˆู„ ุงู„ู„ู‡ ๏ทบ : (ุฅู† ุงู„ู„ู‡ ูˆู…ู„ุงุฆูƒุชู‡ ูŠุตู„ูˆู† ุนู„ู‰ ุงู„ุตู ุงู„ุฃูˆู„) ู‚ุงู„ูˆุง: ูŠุง ุฑุณูˆู„ ุงู„ู„ู‡ ูˆุนู„ู‰ ุงู„ุซุงู†ูŠุŸ ู‚ุงู„: (ุฅู† ุงู„ู„ู‡ ูˆู…ู„ุงุฆูƒุชู‡ ูŠุตู„ูˆู† ุนู„ู‰ ุงู„ุตู ุงู„ุฃูˆู„). ู‚ุงู„ูˆุง: ูŠุง ุฑุณูˆู„ ุงู„ู„ู‡ ูˆุนู„ู‰ ุงู„ุซุงู†ูŠุŸ ู‚ุงู„: (ูˆุนู„ู‰ ุงู„ุซุงู†ูŠ). ุฃุฎุฑุฌู‡ ุฃุญู…ุฏ'],
    ['ู…ุง ู‡ูŠ ุงู„ุนู„ุงู…ุฉ ุงู„ุชูŠ ุฅุฐุง ุธู‡ุฑุช ุฃุบู„ู‚ ุจุงุจ ุงู„ุชูˆุจุฉ ุŸ', 'ุญุฏูŠุซ ุงุจู’ู†ู ุนูŽุจู‘ูŽุงุณู ุฑุถูŠ ุงู„ู„ู‡ ุนู†ู‡ ู‚ูŽุงู„ูŽ: ยซุฃูู†ู’ุฒูู„ูŽ ุนูŽู„ูŽู‰ ุฑูŽุณููˆู„ู ุงู„ู„ู‡ ๏ทบ ูˆูŽู‡ููˆูŽ ุงุจู’ู†ู ุฃูŽุฑู’ุจูŽุนููŠู†ูŽุŒ ููŽู…ูŽูƒูŽุซูŽ ุจูู…ูŽูƒู‘ูŽุฉูŽ ุซูŽู„ุงูŽุซูŽ ุนูŽุดู’ุฑูŽุฉูŽ ุณูŽู†ูŽุฉู‹ุŒ ุซูู…ู‘ูŽ ุฃูู…ูุฑูŽ ุจูุงู„ู‡ูุฌู’ุฑูŽุฉู ููŽู‡ูŽุงุฌูŽุฑูŽ ุฅูู„ูŽู‰ ุงู„ู…ูŽุฏููŠู†ูŽุฉูุŒ ููŽู…ูŽูƒูŽุซูŽ ุจูู‡ูŽุง ุนูŽุดู’ุฑูŽ ุณูู†ููŠู†ูŽุŒ ุซูู…ู‘ูŽ ุชููˆููู‘ููŠูŽ ๏ทบ ยป. ุฑูˆุงู‡ ุงู„ุจุฎุงุฑูŠ (3851)ุŒ ูˆู…ุณู„ู… (2351).'],
    ['ุฃูŠู† ุชุตู„ู‰ ุงู„ูุฑุงุฆุถ ุŸ', 'ุญุฏูŠุซ ุฃูŽุจููŠ ู‡ูุฑูŽูŠู’ุฑูŽุฉูŽ ุฑุถูŠ ุงู„ู„ู‡ ุนู†ู‡ุŒ ุฃูŽู†ู‘ูŽ ุงู„ู†ู‘ูŽุจููŠู‘ูŽ ๏ทบ ู‚ูŽุงู„ูŽ: ยซุฎูŽูŠู’ุฑู ูŠูŽูˆู’ู…ู ุทูŽู„ูŽุนูŽุชู’ ุนูŽู„ูŽูŠู’ู‡ู ุงู„ุดู‘ูŽู…ู’ุณู ูŠูŽูˆู’ู…ู ุงู„ุฌูู…ูุนูŽุฉูุŒ ูููŠู‡ู ุฎูู„ูู‚ูŽ ุขุฏูŽู…ูุŒ ูˆูŽูููŠู‡ู ุฃูุฏู’ุฎูู„ูŽ ุงู„ุฌูŽู†ู‘ูŽุฉูŽุŒ ูˆูŽูููŠู‡ู ุฃูุฎู’ุฑูุฌูŽ ู…ูู†ู’ู‡ูŽุงยป. ุฑูˆุงู‡ ู…ุณู„ู… (854).'],
    ['ุงุฐูƒุฑ ูƒูŠููŠุฉ ุงู„ุชูŠู…ู… ุŸ', 'ุนู† ุงู„ู†ุจูŠ ๏ทบ ู‚ุงู„: (ุฅู† ุฃูˆู„ ู…ุง ูŠุญุงุณุจ ุนู„ูŠู‡ ุงู„ุนุจุฏ ูŠูˆู… ุงู„ู‚ูŠุงู…ุฉ ู…ู† ุนู…ู„ู‡ ุตู„ุงุชู‡ุŒ ูุฅู† ุตู„ุญุช ูู‚ุฏ ุฃูู„ุญ ูˆู†ุฌุญุŒ ูˆุฅู† ูุณุฏุช ูู‚ุฏ ุฎุงุจ ูˆุฎุณุฑุŒ ูุฅู† ุงู†ุชู‚ุต ู…ู† ูุฑูŠุถุชู‡ ุดูŠุก ู‚ุงู„ ุงู„ุฑุจู‘ ุนุฒ ูˆุฌู„: ุงู†ุธุฑูˆุง ู‡ู„ ู„ุนุจุฏูŠ ู…ู† ุชุทูˆุน ููŠูƒู…ู„ ุจู‡ุง ู…ุง ุงู†ุชู‚ุต ู…ู† ุงู„ูุฑูŠุถุฉุŒ ุซู… ูŠูƒูˆู† ุณุงุฆุฑ ุนู…ู„ู‡ ุนู„ู‰ ุฐู„ูƒ).     ุณู†ู† ุงุจู† ู…ุงุฌู‡ ูˆุงู„ุชุฑู…ุฐูŠ'],
]
scores = model.predict(pairs)
print(scores.shape)
# (5,)

# Or rank different texts based on similarity to a single text
ranks = model.rank(
    'ู…ุง ุงู„ุฏุนุงุก ุงู„ูˆุงุฑุฏ ุนู†ุฏ ุงู„ุฏุฎูˆู„ ูˆุงู„ุฎุฑูˆุฌ ู…ู† ุงู„ู…ุณุฌุฏุŸ',
    [
        'ุญุฏูŠุซ ุนูŽู†ู’ ุนูู…ูŽุฑูŽ ุจู’ู†ู ุงู„ุฎูŽุทู‘ูŽุงุจู ุฑุถูŠ ุงู„ู„ู‡ ุนู†ู‡ุŒ ู‚ูŽุงู„ูŽ: ู‚ูŽุงู„ูŽ ุฑูŽุณููˆู„ู ุงู„ู„ู‡ ๏ทบ: ยซู…ูŽุง ู…ูู†ู’ูƒูู…ู’ ู…ูู†ู’ ุฃูŽุญูŽุฏู ูŠูŽุชูŽูˆูŽุถู‘ูŽุฃู ููŽูŠูุจู’ู„ูุบู - ุฃูŽูˆู’ ููŽูŠูุณู’ุจูุบู - ุงู„ูˆูŽุถููˆุกูŽ ุซูู…ู‘ูŽ ูŠูŽู‚ููˆู„ู: ุฃูŽุดู’ู‡ูŽุฏู ุฃูŽู†ู’ ู„ูŽุง ุฅูู„ูŽู‡ูŽ ุฅูู„ู‘ูŽุง ุงู„ู„ู‡ ูˆูŽุฃูŽู†ู‘ูŽ ู…ูุญูŽู…ู‘ูŽุฏู‹ุง ุนูŽุจู’ุฏู ุงู„ู„ู‡ ูˆูŽุฑูŽุณููˆู„ูู‡ู ุฅูู„ู‘ูŽุง ููุชูุญูŽุชู’ ู„ูŽู‡ู ุฃูŽุจู’ูˆูŽุงุจู ุงู„ุฌูŽู†ู‘ูŽุฉู ุงู„ุซู‘ูŽู…ูŽุงู†ููŠูŽุฉู ูŠูŽุฏู’ุฎูู„ู ู…ูู†ู’ ุฃูŽูŠู‘ูู‡ูŽุง ุดูŽุงุกูŽยป. ุฑูˆุงู‡ ู…ุณู„ู… (234).',
        'ุญุฏูŠุซ ุฃุจูŠ ุฃู…ุงู…ุฉ ุฑุถูŠ ุงู„ู„ู‡ ุนู†ู‡ ู‚ุงู„: ู‚ุงู„ ุฑุณูˆู„ ุงู„ู„ู‡ ๏ทบ : (ุฅู† ุงู„ู„ู‡ ูˆู…ู„ุงุฆูƒุชู‡ ูŠุตู„ูˆู† ุนู„ู‰ ุงู„ุตู ุงู„ุฃูˆู„) ู‚ุงู„ูˆุง: ูŠุง ุฑุณูˆู„ ุงู„ู„ู‡ ูˆุนู„ู‰ ุงู„ุซุงู†ูŠุŸ ู‚ุงู„: (ุฅู† ุงู„ู„ู‡ ูˆู…ู„ุงุฆูƒุชู‡ ูŠุตู„ูˆู† ุนู„ู‰ ุงู„ุตู ุงู„ุฃูˆู„). ู‚ุงู„ูˆุง: ูŠุง ุฑุณูˆู„ ุงู„ู„ู‡ ูˆุนู„ู‰ ุงู„ุซุงู†ูŠุŸ ู‚ุงู„: (ูˆุนู„ู‰ ุงู„ุซุงู†ูŠ). ุฃุฎุฑุฌู‡ ุฃุญู…ุฏ',
        'ุญุฏูŠุซ ุงุจู’ู†ู ุนูŽุจู‘ูŽุงุณู ุฑุถูŠ ุงู„ู„ู‡ ุนู†ู‡ ู‚ูŽุงู„ูŽ: ยซุฃูู†ู’ุฒูู„ูŽ ุนูŽู„ูŽู‰ ุฑูŽุณููˆู„ู ุงู„ู„ู‡ ๏ทบ ูˆูŽู‡ููˆูŽ ุงุจู’ู†ู ุฃูŽุฑู’ุจูŽุนููŠู†ูŽุŒ ููŽู…ูŽูƒูŽุซูŽ ุจูู…ูŽูƒู‘ูŽุฉูŽ ุซูŽู„ุงูŽุซูŽ ุนูŽุดู’ุฑูŽุฉูŽ ุณูŽู†ูŽุฉู‹ุŒ ุซูู…ู‘ูŽ ุฃูู…ูุฑูŽ ุจูุงู„ู‡ูุฌู’ุฑูŽุฉู ููŽู‡ูŽุงุฌูŽุฑูŽ ุฅูู„ูŽู‰ ุงู„ู…ูŽุฏููŠู†ูŽุฉูุŒ ููŽู…ูŽูƒูŽุซูŽ ุจูู‡ูŽุง ุนูŽุดู’ุฑูŽ ุณูู†ููŠู†ูŽุŒ ุซูู…ู‘ูŽ ุชููˆููู‘ููŠูŽ ๏ทบ ยป. ุฑูˆุงู‡ ุงู„ุจุฎุงุฑูŠ (3851)ุŒ ูˆู…ุณู„ู… (2351).',
        'ุญุฏูŠุซ ุฃูŽุจููŠ ู‡ูุฑูŽูŠู’ุฑูŽุฉูŽ ุฑุถูŠ ุงู„ู„ู‡ ุนู†ู‡ุŒ ุฃูŽู†ู‘ูŽ ุงู„ู†ู‘ูŽุจููŠู‘ูŽ ๏ทบ ู‚ูŽุงู„ูŽ: ยซุฎูŽูŠู’ุฑู ูŠูŽูˆู’ู…ู ุทูŽู„ูŽุนูŽุชู’ ุนูŽู„ูŽูŠู’ู‡ู ุงู„ุดู‘ูŽู…ู’ุณู ูŠูŽูˆู’ู…ู ุงู„ุฌูู…ูุนูŽุฉูุŒ ูููŠู‡ู ุฎูู„ูู‚ูŽ ุขุฏูŽู…ูุŒ ูˆูŽูููŠู‡ู ุฃูุฏู’ุฎูู„ูŽ ุงู„ุฌูŽู†ู‘ูŽุฉูŽุŒ ูˆูŽูููŠู‡ู ุฃูุฎู’ุฑูุฌูŽ ู…ูู†ู’ู‡ูŽุงยป. ุฑูˆุงู‡ ู…ุณู„ู… (854).',
        'ุนู† ุงู„ู†ุจูŠ ๏ทบ ู‚ุงู„: (ุฅู† ุฃูˆู„ ู…ุง ูŠุญุงุณุจ ุนู„ูŠู‡ ุงู„ุนุจุฏ ูŠูˆู… ุงู„ู‚ูŠุงู…ุฉ ู…ู† ุนู…ู„ู‡ ุตู„ุงุชู‡ุŒ ูุฅู† ุตู„ุญุช ูู‚ุฏ ุฃูู„ุญ ูˆู†ุฌุญุŒ ูˆุฅู† ูุณุฏุช ูู‚ุฏ ุฎุงุจ ูˆุฎุณุฑุŒ ูุฅู† ุงู†ุชู‚ุต ู…ู† ูุฑูŠุถุชู‡ ุดูŠุก ู‚ุงู„ ุงู„ุฑุจู‘ ุนุฒ ูˆุฌู„: ุงู†ุธุฑูˆุง ู‡ู„ ู„ุนุจุฏูŠ ู…ู† ุชุทูˆุน ููŠูƒู…ู„ ุจู‡ุง ู…ุง ุงู†ุชู‚ุต ู…ู† ุงู„ูุฑูŠุถุฉุŒ ุซู… ูŠูƒูˆู† ุณุงุฆุฑ ุนู…ู„ู‡ ุนู„ู‰ ุฐู„ูƒ).     ุณู†ู† ุงุจู† ู…ุงุฌู‡ ูˆุงู„ุชุฑู…ุฐูŠ',
    ]
)
# [{'corpus_id': ..., 'score': ...}, {'corpus_id': ..., 'score': ...}, ...]

Evaluation

Metrics

Cross Encoder Classification

Metric Value
accuracy 0.9347
accuracy_threshold 0.6417
f1 0.8669
f1_threshold 0.3031
precision 0.8643
recall 0.8694
average_precision 0.9278

Cross Encoder Classification

Metric Value
accuracy 0.8697
accuracy_threshold 0.4259
f1 0.4238
f1_threshold 0.2148
precision 0.4507
recall 0.4
average_precision 0.5014

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: 34.89 characters
    • max: 113 characters
    • min: 39 characters
    • mean: 276.97 characters
    • max: 12335 characters
    • min: 0.0
    • mean: 0.16
    • max: 1.0
  • Samples:
    sentence_0 sentence_1 label
    ู…ุง ุงู„ุฏุนุงุก ุงู„ูˆุงุฑุฏ ุนู†ุฏ ุงู„ุฏุฎูˆู„ ูˆุงู„ุฎุฑูˆุฌ ู…ู† ุงู„ู…ุณุฌุฏุŸ ุญุฏูŠุซ ุนูŽู†ู’ ุนูู…ูŽุฑูŽ ุจู’ู†ู ุงู„ุฎูŽุทู‘ูŽุงุจู ุฑุถูŠ ุงู„ู„ู‡ ุนู†ู‡ุŒ ู‚ูŽุงู„ูŽ: ู‚ูŽุงู„ูŽ ุฑูŽุณููˆู„ู ุงู„ู„ู‡ ๏ทบ: ยซู…ูŽุง ู…ูู†ู’ูƒูู…ู’ ู…ูู†ู’ ุฃูŽุญูŽุฏู ูŠูŽุชูŽูˆูŽุถู‘ูŽุฃู ููŽูŠูุจู’ู„ูุบู - ุฃูŽูˆู’ ููŽูŠูุณู’ุจูุบู - ุงู„ูˆูŽุถููˆุกูŽ ุซูู…ู‘ูŽ ูŠูŽู‚ููˆู„ู: ุฃูŽุดู’ู‡ูŽุฏู ุฃูŽู†ู’ ู„ูŽุง ุฅูู„ูŽู‡ูŽ ุฅูู„ู‘ูŽุง ุงู„ู„ู‡ ูˆูŽุฃูŽู†ู‘ูŽ ู…ูุญูŽู…ู‘ูŽุฏู‹ุง ุนูŽุจู’ุฏู ุงู„ู„ู‡ ูˆูŽุฑูŽุณููˆู„ูู‡ู ุฅูู„ู‘ูŽุง ููุชูุญูŽุชู’ ู„ูŽู‡ู ุฃูŽุจู’ูˆูŽุงุจู ุงู„ุฌูŽู†ู‘ูŽุฉู ุงู„ุซู‘ูŽู…ูŽุงู†ููŠูŽุฉู ูŠูŽุฏู’ุฎูู„ู ู…ูู†ู’ ุฃูŽูŠู‘ูู‡ูŽุง ุดูŽุงุกูŽยป. ุฑูˆุงู‡ ู…ุณู„ู… (234). 0.0
    ู…ุง ุญูƒู… ู…ู† ู„ู… ูŠู‚ุฑุฃ ุจูุงุชุญุฉ ุงู„ูƒุชุงุจ ุŸ ุญุฏูŠุซ ุฃุจูŠ ุฃู…ุงู…ุฉ ุฑุถูŠ ุงู„ู„ู‡ ุนู†ู‡ ู‚ุงู„: ู‚ุงู„ ุฑุณูˆู„ ุงู„ู„ู‡ ๏ทบ : (ุฅู† ุงู„ู„ู‡ ูˆู…ู„ุงุฆูƒุชู‡ ูŠุตู„ูˆู† ุนู„ู‰ ุงู„ุตู ุงู„ุฃูˆู„) ู‚ุงู„ูˆุง: ูŠุง ุฑุณูˆู„ ุงู„ู„ู‡ ูˆุนู„ู‰ ุงู„ุซุงู†ูŠุŸ ู‚ุงู„: (ุฅู† ุงู„ู„ู‡ ูˆู…ู„ุงุฆูƒุชู‡ ูŠุตู„ูˆู† ุนู„ู‰ ุงู„ุตู ุงู„ุฃูˆู„). ู‚ุงู„ูˆุง: ูŠุง ุฑุณูˆู„ ุงู„ู„ู‡ ูˆุนู„ู‰ ุงู„ุซุงู†ูŠุŸ ู‚ุงู„: (ูˆุนู„ู‰ ุงู„ุซุงู†ูŠ). ุฃุฎุฑุฌู‡ ุฃุญู…ุฏ 0.0
    ู…ุง ู‡ูŠ ุงู„ุนู„ุงู…ุฉ ุงู„ุชูŠ ุฅุฐุง ุธู‡ุฑุช ุฃุบู„ู‚ ุจุงุจ ุงู„ุชูˆุจุฉ ุŸ ุญุฏูŠุซ ุงุจู’ู†ู ุนูŽุจู‘ูŽุงุณู ุฑุถูŠ ุงู„ู„ู‡ ุนู†ู‡ ู‚ูŽุงู„ูŽ: ยซุฃูู†ู’ุฒูู„ูŽ ุนูŽู„ูŽู‰ ุฑูŽุณููˆู„ู ุงู„ู„ู‡ ๏ทบ ูˆูŽู‡ููˆูŽ ุงุจู’ู†ู ุฃูŽุฑู’ุจูŽุนููŠู†ูŽุŒ ููŽู…ูŽูƒูŽุซูŽ ุจูู…ูŽูƒู‘ูŽุฉูŽ ุซูŽู„ุงูŽุซูŽ ุนูŽุดู’ุฑูŽุฉูŽ ุณูŽู†ูŽุฉู‹ุŒ ุซูู…ู‘ูŽ ุฃูู…ูุฑูŽ ุจูุงู„ู‡ูุฌู’ุฑูŽุฉู ููŽู‡ูŽุงุฌูŽุฑูŽ ุฅูู„ูŽู‰ ุงู„ู…ูŽุฏููŠู†ูŽุฉูุŒ ููŽู…ูŽูƒูŽุซูŽ ุจูู‡ูŽุง ุนูŽุดู’ุฑูŽ ุณูู†ููŠู†ูŽุŒ ุซูู…ู‘ูŽ ุชููˆููู‘ููŠูŽ ๏ทบ ยป. ุฑูˆุงู‡ ุงู„ุจุฎุงุฑูŠ (3851)ุŒ ูˆู…ุณู„ู… (2351). 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
  • per_device_train_batch_size: 16
  • per_device_eval_batch_size: 16
  • 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: 16
  • per_device_eval_batch_size: 16
  • 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

Training Logs

Epoch Step Training Loss eval_average_precision
0.6596 500 0.3554 0.8973
1.0 758 - 0.9116
1.3193 1000 0.2635 0.9163
1.9789 1500 0.2561 0.9224
2.0 1516 - 0.9227
2.6385 2000 0.2284 0.9248
3.0 2274 - 0.9270
3.2982 2500 0.2316 0.9275
3.9578 3000 0.2068 0.9278
4.0 3032 - 0.9278
0.9276 500 0.4336 0.4935
1.0 539 - 0.4931
1.8553 1000 0.361 0.5021
2.0 1078 - 0.5014
2.7829 1500 0.3574 0.5006
3.0 1617 - 0.5019
3.7106 2000 0.352 0.5016
4.0 2156 - 0.5014

Framework Versions

  • Python: 3.11.13
  • Sentence Transformers: 4.1.0
  • Transformers: 4.54.0
  • PyTorch: 2.6.0+cu124
  • Accelerate: 1.9.0
  • Datasets: 4.0.0
  • Tokenizers: 0.21.2

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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