--- tags: - sentence-transformers - cross-encoder - reranker - generated_from_trainer - dataset_size:68056 - loss:BinaryCrossEntropyLoss base_model: BAAI/bge-reranker-v2-m3 pipeline_tag: text-ranking library_name: sentence-transformers metrics: - accuracy - accuracy_threshold - f1 - f1_threshold - precision - recall - average_precision model-index: - name: CrossEncoder based on BAAI/bge-reranker-v2-m3 results: - task: type: cross-encoder-binary-classification name: Cross Encoder Binary Classification dataset: name: eval type: eval metrics: - type: accuracy value: 0.8962388216728038 name: Accuracy - type: accuracy_threshold value: 0.2969196140766144 name: Accuracy Threshold - type: f1 value: 0.7976337194971654 name: F1 - type: f1_threshold value: 0.20159849524497986 name: F1 Threshold - type: precision value: 0.7504638218923934 name: Precision - type: recall value: 0.8511309836927933 name: Recall - type: average_precision value: 0.8668698311259357 name: Average Precision --- # CrossEncoder based on BAAI/bge-reranker-v2-m3 This is a [Cross Encoder](https://www.sbert.net/docs/cross_encoder/usage/usage.html) model finetuned from [BAAI/bge-reranker-v2-m3](https://huggingface.co/BAAI/bge-reranker-v2-m3) 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-v2-m3](https://huggingface.co/BAAI/bge-reranker-v2-m3) - **Maximum Sequence Length:** 512 tokens - **Number of Output Labels:** 1 label ### 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) ## 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 texts pairs = [ ['A Hydro Flask in a light brown color with a small hand logo.', 'A large, light-brown Hydro Flask water bottle with a darker tan cap and black accents, appears to be made of metal, and seems to be in new condition with tags still attached.'], ['A black smartphone.', 'The image shows four used smartphones, including a white and black Samsung smartphone, a black and silver phone of unknown brand, a white and black Nokia phone, and a white Apple iPhone, all appearing to be between 4 and 5 inches in screen size.'], ['A purple pencil case with a unicorn design.', 'A new, mint green hard-shell pencil case with a ribbed texture and a central circular illustration of a unicorn with a rainbow mane.'], ['A folded, dark blue umbrella has a slightly crinkled matching fabric case and its handle is still wrapped in clear plastic.', 'There are two blue umbrellas.'], ['a black messenger bag with purple stitching.', 'A gray-green backpack with black mesh padding and an orange "NANEU PRO" tag on the side.'], ] scores = model.predict(pairs) print(scores.shape) # (5,) # Or rank different texts based on similarity to a single text ranks = model.rank( 'A Hydro Flask in a light brown color with a small hand logo.', [ 'A large, light-brown Hydro Flask water bottle with a darker tan cap and black accents, appears to be made of metal, and seems to be in new condition with tags still attached.', 'The image shows four used smartphones, including a white and black Samsung smartphone, a black and silver phone of unknown brand, a white and black Nokia phone, and a white Apple iPhone, all appearing to be between 4 and 5 inches in screen size.', 'A new, mint green hard-shell pencil case with a ribbed texture and a central circular illustration of a unicorn with a rainbow mane.', 'There are two blue umbrellas.', 'A gray-green backpack with black mesh padding and an orange "NANEU PRO" tag on the side.', ] ) # [{'corpus_id': ..., 'score': ...}, {'corpus_id': ..., 'score': ...}, ...] ``` ## Evaluation ### Metrics #### Cross Encoder Binary Classification * Dataset: `eval` * Evaluated with [CEBinaryClassificationEvaluator](https://sbert.net/docs/package_reference/cross_encoder/evaluation.html#sentence_transformers.cross_encoder.evaluation.CEBinaryClassificationEvaluator) | Metric | Value | |:----------------------|:-----------| | accuracy | 0.8962 | | accuracy_threshold | 0.2969 | | f1 | 0.7976 | | f1_threshold | 0.2016 | | precision | 0.7505 | | recall | 0.8511 | | **average_precision** | **0.8669** | ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 68,056 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 | | | | * Samples: | sentence_0 | sentence_1 | label | |:--------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------| | A Hydro Flask in a light brown color with a small hand logo. | A large, light-brown Hydro Flask water bottle with a darker tan cap and black accents, appears to be made of metal, and seems to be in new condition with tags still attached. | 1.0 | | A black smartphone. | The image shows four used smartphones, including a white and black Samsung smartphone, a black and silver phone of unknown brand, a white and black Nokia phone, and a white Apple iPhone, all appearing to be between 4 and 5 inches in screen size. | 0.0 | | A purple pencil case with a unicorn design. | A new, mint green hard-shell pencil case with a ribbed texture and a central circular illustration of a unicorn with a rainbow mane. | 0.0 | * 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 - `eval_strategy`: steps - `per_device_train_batch_size`: 16 - `per_device_eval_batch_size`: 16 #### 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`: 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 - `bf16`: False - `fp16`: False - `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} - `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 - `project`: huggingface - `trackio_space_id`: trackio - `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`: no - `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`: True - `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 | |:------:|:-----:|:-------------:|:----------------------:| | 0.1175 | 500 | 0.3493 | 0.7918 | | 0.2351 | 1000 | 0.3064 | 0.8216 | | 0.3526 | 1500 | 0.2832 | 0.8328 | | 0.4701 | 2000 | 0.2873 | 0.8408 | | 0.5877 | 2500 | 0.2866 | 0.8502 | | 0.7052 | 3000 | 0.2797 | 0.8499 | | 0.8228 | 3500 | 0.2737 | 0.8525 | | 0.9403 | 4000 | 0.2724 | 0.8563 | | 1.0 | 4254 | - | 0.8587 | | 1.0578 | 4500 | 0.2718 | 0.8565 | | 1.1754 | 5000 | 0.264 | 0.8561 | | 1.2929 | 5500 | 0.2642 | 0.8584 | | 1.4104 | 6000 | 0.2604 | 0.8582 | | 1.5280 | 6500 | 0.2593 | 0.8595 | | 1.6455 | 7000 | 0.2498 | 0.8628 | | 1.7630 | 7500 | 0.2515 | 0.8649 | | 1.8806 | 8000 | 0.2504 | 0.8650 | | 1.9981 | 8500 | 0.2624 | 0.8643 | | 2.0 | 8508 | - | 0.8632 | | 2.1157 | 9000 | 0.2481 | 0.8662 | | 2.2332 | 9500 | 0.2483 | 0.8661 | | 2.3507 | 10000 | 0.2543 | 0.8647 | | 2.4683 | 10500 | 0.2473 | 0.8669 | ### Framework Versions - Python: 3.12.10 - Sentence Transformers: 5.1.2 - Transformers: 4.57.1 - PyTorch: 2.9.1+cu128 - Accelerate: 1.11.0 - Datasets: 4.4.1 - Tokenizers: 0.22.1 ## 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", } ```