Bge Reranker Ft

🚀 CrossEncoder based on BAAI/bge-reranker-base

This is a Cross Encoder model that computes scores for text pairs, useful for text reranking and semantic search.

🚀 Quick Start

This is a Cross Encoder model finetuned from BAAI/bge-reranker-base using the sentence-transformers library. It computes scores for pairs of texts, which can be used for text reranking and semantic search.

✨ Features

• Computes scores for text pairs.
• Can be used for text reranking and semantic search.

📦 Installation

First install the Sentence Transformers library:

pip install -U sentence-transformers

💻 Usage Examples

Basic Usage

from sentence_transformers import CrossEncoder

# Download from the 🤗 Hub
model = CrossEncoder("foochun/bge-reranker-ft")
# Get scores for pairs of texts
pairs = [
    ['zach koh yong liang', 'yong liang koh zach'],
    ['zulkifli bin mohamad', 'zulkifli bin muhammad'],
    ['rahman bin mohd rashid', 'rahman mohammed rashid'],
    ['mohd syukri bin bakar', 'muhd syukri bakar'],
    ['carmen tan fang kiat', 'tan fang kiat'],
]
scores = model.predict(pairs)
print(scores.shape)
# (5,)

# Or rank different texts based on similarity to a single text
ranks = model.rank(
    'zach koh yong liang',
    [
        'yong liang koh zach',
        'zulkifli bin muhammad',
        'rahman mohammed rashid',
        'muhd syukri bakar',
        'tan fang kiat',
    ]
)
# [{'corpus_id': ..., 'score': ...}, {'corpus_id': ..., 'score': ...}, ...]

📚 Documentation

Model Details

Model Description

Model Sources

• Documentation: Sentence Transformers Documentation
• Documentation: Cross Encoder Documentation
• Repository: Sentence Transformers on GitHub
• Hugging Face: Cross Encoders on Hugging Face

Training Details

Training Dataset

Unnamed Dataset

• Size: 72,905 training samples
• Columns: query, pos, and neg
• Approximate statistics based on the first 1000 samples: | | query | pos | neg | |:--------|:----------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------| | type | string | string | string | | details |
  • min: 9 characters
  • mean: 19.91 characters
  • max: 45 characters
  |
  • min: 9 characters
  • mean: 17.64 characters
  • max: 40 characters
  |
  • min: 9 characters
  • mean: 17.95 characters
  • max: 37 characters
  |
• Samples: | query | pos | neg | |:-------------------------------------------|:-------------------------------------|:-----------------------------------| | sim hong soon | sim hong soon | sim soon hong | | raja mariam binti raja sharif | raja mariam raja sharif | zuraidah binti dollah | | saw ann fui | fui saw ann | ann saw fui |
• Loss: MultipleNegativesRankingLoss with these parameters:

{
    "scale": 10.0,
    "num_negatives": 4,
    "activation_fn": "torch.nn.modules.activation.Sigmoid"
}

Evaluation Dataset

Unnamed Dataset

• Size: 10,415 evaluation samples
• Columns: query, pos, and neg
• Approximate statistics based on the first 1000 samples: | | query | pos | neg | |:--------|:----------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------| | type | string | string | string | | details |
  • min: 9 characters
  • mean: 19.95 characters
  • max: 43 characters
  |
  • min: 9 characters
  • mean: 17.8 characters
  • max: 42 characters
  |
  • min: 8 characters
  • mean: 18.33 characters
  • max: 36 characters
  |
• Samples: | query | pos | neg | |:------------------------------------|:------------------------------------|:---------------------------------| | zach koh yong liang | yong liang koh zach | liang yong koh zach | | zulkifli bin mohamad | zulkifli bin muhammad | razak bin ibrahim | | rahman bin mohd rashid | rahman mohammed rashid | fauzi bin mohd |
• Loss: MultipleNegativesRankingLoss with these parameters:

{
    "scale": 10.0,
    "num_negatives": 4,
    "activation_fn": "torch.nn.modules.activation.Sigmoid"
}

Training Hyperparameters

Non-Default Hyperparameters

• eval_strategy: steps
• per_device_train_batch_size: 64
• per_device_eval_batch_size: 64
• learning_rate: 1e-05
• warmup_ratio: 0.1
• seed: 12
• fp16: True
• dataloader_num_workers: 4
• load_best_model_at_end: True
• batch_sampler: no_duplicates

All Hyperparameters

Training Logs

Framework Versions

• Python: 3.11.9
• Sentence Transformers: 4.1.0
• Transformers: 4.51.3
• PyTorch: 2.6.0+cu124
• Accelerate: 1.6.0
• Datasets: 3.6.0
• Tokenizers: 0.21.1

📄 License

No license information provided.

📖 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",
}