--- tags: - sentence-transformers - cross-encoder - reranker - generated_from_trainer - dataset_size:31340 - loss:BinaryCrossEntropyLoss base_model: BAAI/bge-reranker-base pipeline_tag: text-ranking library_name: sentence-transformers --- # CrossEncoder based on BAAI/bge-reranker-base This is a [Cross Encoder](https://www.sbert.net/docs/cross_encoder/usage/usage.html) model finetuned from [BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base) using the [sentence-transformers](https://www.SBERT.net) 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:** [BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base) - **Maximum Sequence Length:** 128 tokens - **Number of Output Labels:** 1 label - **Supported Modality:** Text ### Model Sources - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) - **Documentation:** [Cross Encoder Documentation](https://www.sbert.net/docs/cross_encoder/usage/usage.html) - **Repository:** [Sentence Transformers on GitHub](https://github.com/huggingface/sentence-transformers) - **Hugging Face:** [Cross Encoders on Hugging Face](https://huggingface.co/models?library=sentence-transformers&other=cross-encoder) ### Full Model Architecture ``` CrossEncoder( (0): Transformer({'transformer_task': 'sequence-classification', 'modality_config': {'text': {'method': 'forward', 'method_output_name': 'logits'}}, 'module_output_name': 'scores', 'architecture': 'XLMRobertaForSequenceClassification'}) ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import CrossEncoder # Download from the 🤗 Hub model = CrossEncoder("cross_encoder_model_id") # Get scores for pairs of inputs pairs = [ ['حقيبة تشانك لوكس', "Globus Women's Textured Vegan Leather Sling Bag Tan | Globus Women Tan Vegan Leather Textured Sling Bag With Detachable Strap | globus | Crossbody Bags | Tan"], ['حريمية ماسكات كورية', 'Kappa 3-Pack Crew Socks Multicolour | Kappa Pack of 3 Crew Length Socks | Kappa | Socks | Multicolour'], ['شسي غير مبطنة', 'Fall In Love Unlined Bodysuit | فول إن لوف بودي سوت غير مبطن | DeFacto | Body Suits | Deep Magenta'], ['كندرة رموش مريحة للستات', 'Lift N Snatch Brow Tint Pen Black | قلم تحديد الحواجب ليفت أند سناتش رمادي أسود | NYX PROFESSIONAL MAKEUP | All Products | Black'], ['white blouse', '2Xtremz Schiffli Ruffle Cotton Top White | 2Xtremz Regular Fit Cotton Top with Schiffli and Ruffle Detail | 2Xtremz | Blouses | White'], ] scores = model.predict(pairs) print(scores) # [0.9418 0.0044 0.978 0.2881 0.9463] # Or rank different texts based on similarity to a single text ranks = model.rank( 'حقيبة تشانك لوكس', [ "Globus Women's Textured Vegan Leather Sling Bag Tan | Globus Women Tan Vegan Leather Textured Sling Bag With Detachable Strap | globus | Crossbody Bags | Tan", 'Kappa 3-Pack Crew Socks Multicolour | Kappa Pack of 3 Crew Length Socks | Kappa | Socks | Multicolour', 'Fall In Love Unlined Bodysuit | فول إن لوف بودي سوت غير مبطن | DeFacto | Body Suits | Deep Magenta', 'Lift N Snatch Brow Tint Pen Black | قلم تحديد الحواجب ليفت أند سناتش رمادي أسود | NYX PROFESSIONAL MAKEUP | All Products | Black', '2Xtremz Schiffli Ruffle Cotton Top White | 2Xtremz Regular Fit Cotton Top with Schiffli and Ruffle Detail | 2Xtremz | Blouses | White', ] ) # [{'corpus_id': ..., 'score': ...}, {'corpus_id': ..., 'score': ...}, ...] ``` ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 31,340 training samples * Columns: sentence_0, sentence_1, and label * Approximate statistics based on the first 100 samples: | | sentence_0 | sentence_1 | label | |:---------|:---------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:---------------------------------------------------------------| | type | string | string | float | | modality | text | text | | | details | | | | * Samples: | sentence_0 | sentence_1 | label | |:---------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------| | حقيبة تشانك لوكس | Globus Women's Textured Vegan Leather Sling Bag Tan \| Globus Women Tan Vegan Leather Textured Sling Bag With Detachable Strap \| globus \| Crossbody Bags \| Tan | 1.0 | | حريمية ماسكات كورية | Kappa 3-Pack Crew Socks Multicolour \| Kappa Pack of 3 Crew Length Socks \| Kappa \| Socks \| Multicolour | 0.0 | | شسي غير مبطنة | Fall In Love Unlined Bodysuit \| فول إن لوف بودي سوت غير مبطن \| DeFacto \| Body Suits \| Deep Magenta | 1.0 | * Loss: [BinaryCrossEntropyLoss](https://sbert.net/docs/package_reference/cross_encoder/losses.html#binarycrossentropyloss) with these parameters: ```json { "activation_fn": "torch.nn.modules.linear.Identity", "pos_weight": null } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `per_device_train_batch_size`: 32 - `per_device_eval_batch_size`: 32 - `fp16`: True - `disable_tqdm`: True #### All Hyperparameters
Click to expand - `overwrite_output_dir`: False - `do_predict`: False - `prediction_loss_only`: True - `per_device_train_batch_size`: 32 - `per_device_eval_batch_size`: 32 - `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`: 3 - `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`: True - `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 - `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 - `dispatch_batches`: None - `split_batches`: 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 - `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 | |:------:|:----:|:-------------:| | 0.5102 | 500 | 0.6826 | | 1.0204 | 1000 | 0.4261 | | 1.5306 | 1500 | 0.3741 | | 2.0408 | 2000 | 0.3523 | | 2.5510 | 2500 | 0.33 | ### Training Time - **Training**: 5.3 minutes ### Framework Versions - Python: 3.11.12 - Sentence Transformers: 5.5.1 - Transformers: 4.49.0 - PyTorch: 2.7.0+cu128 - Accelerate: 1.13.0 - Datasets: 4.8.5 - Tokenizers: 0.21.4 ## Additional Resources - [Training and Finetuning Reranker Models with Sentence Transformers](https://huggingface.co/blog/train-reranker): the end-to-end guide for training or finetuning Cross Encoder (reranker) models. - [Multimodal Embedding & Reranker Models with Sentence Transformers](https://huggingface.co/blog/multimodal-sentence-transformers): use text, image, audio, and video reranker models through the same API. - [Training and Finetuning Multimodal Embedding & Reranker Models with Sentence Transformers](https://huggingface.co/blog/train-multimodal-sentence-transformers): training multimodal Cross Encoders. ## Citation ### BibTeX #### Sentence Transformers ```bibtex @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", } ```