CrossEncoder based on yoriis/GTE-tydi

This is a Cross Encoder model finetuned from yoriis/GTE-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/GTE-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/GTE-tydi-tafseer-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 1.0
accuracy_threshold 0.8513
f1 1.0
f1_threshold 0.8513
precision 1.0
recall 1.0
average_precision 1.0

Cross Encoder Classification

Metric Value
accuracy 0.9362
accuracy_threshold 0.401
f1 0.8677
f1_threshold 0.2964
precision 0.901
recall 0.8368
average_precision 0.9243

Cross Encoder Classification

Metric Value
accuracy 0.8655
accuracy_threshold 0.8725
f1 0.3968
f1_threshold 0.1597
precision 0.5435
recall 0.3125
average_precision 0.4704

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
0.4386 500 0.1057 1.0
0.8772 1000 0.001 1.0000
1.0 1140 - 1.0
1.3158 1500 0.0008 1.0000
1.7544 2000 0.0005 1.0000
2.0 2280 - 1.0000
2.1930 2500 0.0005 1.0
2.6316 3000 0.0004 1.0
3.0 3420 - 1.0000
3.0702 3500 0.0004 1.0
3.5088 4000 0.0004 1.0000
3.9474 4500 0.0004 1.0
4.0 4560 - 1.0
0.3298 500 0.4486 0.9037
0.6596 1000 0.3242 0.9110
0.9894 1500 0.3305 0.9150
1.0 1516 - 0.9149
1.3193 2000 0.2919 0.9185
1.6491 2500 0.2892 0.9198
1.9789 3000 0.2665 0.9209
2.0 3032 - 0.9208
2.3087 3500 0.2782 0.9219
2.6385 4000 0.2888 0.9229
2.9683 4500 0.2502 0.9234
3.0 4548 - 0.9235
3.2982 5000 0.2584 0.9237
3.6280 5500 0.2487 0.9241
3.9578 6000 0.2701 0.9243
4.0 6064 - 0.9243
0.4638 500 0.6039 0.4515
0.9276 1000 0.4031 0.4523
1.0 1078 - 0.4551
1.3915 1500 0.3894 0.4598
1.8553 2000 0.3705 0.4625
2.0 2156 - 0.4642
2.3191 2500 0.3993 0.4681
2.7829 3000 0.3585 0.4680
3.0 3234 - 0.4688
3.2468 3500 0.3656 0.4679
3.7106 4000 0.3556 0.4706
4.0 4312 - 0.4704

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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