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ce_finetuned_bce

Text Ranking
sentence-transformers
Safetensors
new
cross-encoder
reranker
Generated from Trainer
dataset_size:1548
loss:BinaryCrossEntropyLoss
custom_code
text-embeddings-inference
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metadata 
tags:
  - sentence-transformers
  - cross-encoder
  - reranker
  - generated_from_trainer
  - dataset_size:1548
  - loss:BinaryCrossEntropyLoss
base_model: Alibaba-NLP/gte-multilingual-reranker-base
pipeline_tag: text-ranking
library_name: sentence-transformers

CrossEncoder based on Alibaba-NLP/gte-multilingual-reranker-base

This is a Cross Encoder model finetuned from Alibaba-NLP/gte-multilingual-reranker-base on the json dataset 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 🤗 Hub
model = CrossEncoder("cross_encoder_model_id")
# Get scores for pairs of texts
pairs = [
    ['приймати позу самовдоволеного, виявляючи пиху, зазнайство', 'having a mutual understanding or shared thoughts'],
    ['приймати позу самовдоволеного, виявляючи пиху, зазнайство', 'in trouble or state of shame'],
    ['приймати позу самовдоволеного, виявляючи пиху, зазнайство', 'someone who scrounges from others'],
    ['приймати позу самовдоволеного, виявляючи пиху, зазнайство', 'to subtly and indirectly seek praise, validation, or admiration from others'],
    ['приймати позу самовдоволеного, виявляючи пиху, зазнайство', 'to be on the verge of doing something'],
]
scores = model.predict(pairs)
print(scores.shape)
# (5,)

# Or rank different texts based on similarity to a single text
ranks = model.rank(
    'приймати позу самовдоволеного, виявляючи пиху, зазнайство',
    [
        'having a mutual understanding or shared thoughts',
        'in trouble or state of shame',
        'someone who scrounges from others',
        'to subtly and indirectly seek praise, validation, or admiration from others',
        'to be on the verge of doing something',
    ]
)
# [{'corpus_id': ..., 'score': ...}, {'corpus_id': ..., 'score': ...}, ...]

Training Details

Training Dataset

json

  • Dataset: json
  • Size: 1,548 training samples
  • Columns: text1, text2, and label
  • Approximate statistics based on the first 1000 samples:
    text1 text2 label
    type string string int
    details
    • min: 5 characters
    • mean: 43.72 characters
    • max: 111 characters
    • min: 4 characters
    • mean: 44.34 characters
    • max: 139 characters
    • 0: ~70.00%
    • 1: ~30.00%
  • Samples:
    text1 text2 label
    уживається для вираження повного заперечення; ні уживається для повного заперечення змісту зазначеного слова; зовсім не (треба) 1
    уживається для вираження повного заперечення; ні уживається для вираження заперечення чогось 1
    уживається для вираження повного заперечення; ні уживається для повного заперечення змісту зазначеного слова; зовсім не (розбиратися) 1
  • Loss: BinaryCrossEntropyLoss with these parameters:
    {
    "activation_fn": "torch.nn.modules.linear.Identity",
    "pos_weight": null
}

Evaluation Dataset

json

  • Dataset: json
  • Size: 225 evaluation samples
  • Columns: text1, text2, and label
  • Approximate statistics based on the first 225 samples:
    text1 text2 label
    type string string int
    details
    • min: 10 characters
    • mean: 48.52 characters
    • max: 156 characters
    • min: 6 characters
    • mean: 45.7 characters
    • max: 129 characters
    • 0: ~81.78%
    • 1: ~18.22%
  • Samples:
    text1 text2 label
    приймати позу самовдоволеного, виявляючи пиху, зазнайство having a mutual understanding or shared thoughts 0
    приймати позу самовдоволеного, виявляючи пиху, зазнайство in trouble or state of shame 0
    приймати позу самовдоволеного, виявляючи пиху, зазнайство someone who scrounges from others 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
  • learning_rate: 2e-05
  • num_train_epochs: 2
  • warmup_ratio: 0.1
  • fp16: True
  • load_best_model_at_end: 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: 2e-05
  • weight_decay: 0.0
  • adam_beta1: 0.9
  • adam_beta2: 0.999
  • adam_epsilon: 1e-08
  • max_grad_norm: 1.0
  • num_train_epochs: 2
  • max_steps: -1
  • lr_scheduler_type: linear
  • lr_scheduler_kwargs: {}
  • warmup_ratio: 0.1
  • 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: True
  • 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}
  • parallelism_config: None
  • deepspeed: None
  • label_smoothing_factor: 0.0
  • optim: adamw_torch_fused
  • 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
0.5155 50 0.4717
1.0309 100 0.3624
1.5464 150 0.2148

Framework Versions

  • Python: 3.13.1
  • Sentence Transformers: 5.1.0
  • Transformers: 4.56.1
  • PyTorch: 2.8.0+cpu
  • Accelerate: 1.12.0
  • Datasets: 4.4.1
  • Tokenizers: 0.22.0

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",
}