--- language: - multilingual license: cc-by-nc-4.0 tags: - sentence-transformers - cross-encoder - reranker - generated_from_trainer - dataset_size:17000 - loss:BinaryCrossEntropyLoss base_model: jinaai/jina-reranker-v2-base-multilingual pipeline_tag: text-ranking library_name: sentence-transformers metrics: - map - mrr@10 - ndcg@10 model-index: - name: cometadata/jina-reranker-v2-multilingual-affiliations-large results: - task: type: cross-encoder-reranking name: Cross Encoder Reranking dataset: name: affiliation val type: affiliation-val metrics: - type: map value: 0.981 name: Map - type: mrr@10 value: 0.981 name: Mrr@10 - type: ndcg@10 value: 0.9897 name: Ndcg@10 --- # cometadata/jina-reranker-v2-multilingual-affiliations-large This is a [Cross Encoder](https://www.sbert.net/docs/cross_encoder/usage/usage.html) model finetuned from [jinaai/jina-reranker-v2-base-multilingual](https://huggingface.co/jinaai/jina-reranker-v2-base-multilingual) 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:** [jinaai/jina-reranker-v2-base-multilingual](https://huggingface.co/jinaai/jina-reranker-v2-base-multilingual) - **Maximum Sequence Length:** 1024 tokens - **Number of Output Labels:** 1 label - **Language:** multilingual - **License:** cc-by-nc-4.0 ### 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("cometadata/jina-reranker-v2-multilingual-affiliations-large") # Get scores for pairs of texts pairs = [ ['School of Geographic Sciences and Remote Sensing, Huangpu Research School of Guangzhou University, Guangzhou 510006, China', 'Guangzhou Universityr, Guangzhou, Guangdong, China'], ['School of Geographic Sciences and Remote Sensing, Huangpu Research School of Guangzhou University, Guangzhou 510006, China', 'School of Pharmaceutical Sciences, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China (mainland)'], [' Senior Thoracic Surgeon, Page Chest Pavilion, Royal Prince Alfred Hospital, Sydney.', 'Department of Surgical Oncology, Royal Prince Alfred Hospital, Camperdown, Sydney, Australia'], [' Senior Thoracic Surgeon, Page Chest Pavilion, Royal Prince Alfred Hospital, Sydney.', 'Prince of Wales Hospital Department of Surgery Sydney New South Wales Australia'], ['King Saud University, Plant Protection, King Khalid Road, Riyadh, Riyadh, Saudi Arabia, 2460/11451;', 'Department of Plant Protection, College of Food and Agriculture Sciences, King Saud University, P.O. Box 2460, Riyadh11451, Saudi Arabia'], ] scores = model.predict(pairs) print(scores.shape) # (5,) # Or rank different texts based on similarity to a single text ranks = model.rank( 'School of Geographic Sciences and Remote Sensing, Huangpu Research School of Guangzhou University, Guangzhou 510006, China', [ 'Guangzhou Universityr, Guangzhou, Guangdong, China', 'School of Pharmaceutical Sciences, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China (mainland)', 'Department of Surgical Oncology, Royal Prince Alfred Hospital, Camperdown, Sydney, Australia', 'Prince of Wales Hospital Department of Surgery Sydney New South Wales Australia', 'Department of Plant Protection, College of Food and Agriculture Sciences, King Saud University, P.O. Box 2460, Riyadh11451, Saudi Arabia', ] ) # [{'corpus_id': ..., 'score': ...}, {'corpus_id': ..., 'score': ...}, ...] ``` ## Evaluation ### Metrics #### Cross Encoder Reranking * Dataset: `affiliation-val` * Evaluated with [CrossEncoderRerankingEvaluator](https://sbert.net/docs/package_reference/cross_encoder/evaluation.html#sentence_transformers.cross_encoder.evaluation.CrossEncoderRerankingEvaluator) with these parameters: ```json { "at_k": 10, "always_rerank_positives": true } ``` | Metric | Value | |:------------|:---------------------| | map | 0.9810 (-0.0190) | | mrr@10 | 0.9810 (-0.0190) | | **ndcg@10** | **0.9897 (-0.0103)** | ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 17,000 training samples * Columns: query, document, and label * Approximate statistics based on the first 1000 samples: | | query | document | label | |:--------|:------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------|:------------------------------------------------| | type | string | string | int | | details | | | | * Samples: | query | document | label | |:-------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------|:---------------| | Max-Planck-Institut für Astronomie, Königgstuhl 17, D-69117 Heidelberg, Germany | Max-Planck-Institute for Astronomy, Königstuhl 17, 69117 Heidelberg, Germany e-mail: beuther@mpia.de | 1 | | Max-Planck-Institut für Astronomie, Königgstuhl 17, D-69117 Heidelberg, Germany | Clinical Trials Center Cardiovascular Research Foundation New York City NY USA | 0 | | Instituto de Astrofísica de Canarias, 38200 La Laguna, Tenerife, Spain e-mail: [ldp | Instituto de Astrofísica de Canarias, Avd. Vía Láctea s/n, 38205 La Laguna, Spain | 1 | * 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 } ``` ### Evaluation Dataset #### Unnamed Dataset * Size: 3,000 evaluation samples * Columns: query, document, and label * Approximate statistics based on the first 1000 samples: | | query | document | label | |:--------|:-------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------|:------------------------------------------------| | type | string | string | int | | details | | | | * Samples: | query | document | label | |:----------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------|:---------------| | School of Geographic Sciences and Remote Sensing, Huangpu Research School of Guangzhou University, Guangzhou 510006, China | Guangzhou Universityr, Guangzhou, Guangdong, China | 1 | | School of Geographic Sciences and Remote Sensing, Huangpu Research School of Guangzhou University, Guangzhou 510006, China | School of Pharmaceutical Sciences, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China (mainland) | 0 | | Senior Thoracic Surgeon, Page Chest Pavilion, Royal Prince Alfred Hospital, Sydney. | Department of Surgical Oncology, Royal Prince Alfred Hospital, Camperdown, Sydney, Australia | 1 | * 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`: 128 - `per_device_eval_batch_size`: 128 - `learning_rate`: 3e-05 - `warmup_ratio`: 0.1 - `bf16`: True - `load_best_model_at_end`: True - `push_to_hub`: True - `hub_model_id`: cometadata/jina-reranker-v2-multilingual-affiliations-large #### All Hyperparameters
Click to expand - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: steps - `prediction_loss_only`: True - `per_device_train_batch_size`: 128 - `per_device_eval_batch_size`: 128 - `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`: 3e-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`: 3 - `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 - `bf16`: True - `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`: 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 - `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`: True - `resume_from_checkpoint`: None - `hub_model_id`: cometadata/jina-reranker-v2-multilingual-affiliations-large - `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 - `multi_dataset_batch_sampler`: proportional - `router_mapping`: {} - `learning_rate_mapping`: {}
### Training Logs | Epoch | Step | Training Loss | Validation Loss | affiliation-val_ndcg@10 | |:----------:|:-------:|:-------------:|:---------------:|:-----------------------:| | -1 | -1 | - | - | 0.9273 (-0.0727) | | 0.0075 | 1 | 0.1683 | - | - | | 0.7519 | 100 | 0.2401 | - | - | | 1.5038 | 200 | 0.1296 | - | - | | **1.8797** | **250** | **-** | **0.2779** | **0.9897 (-0.0103)** | | 2.2556 | 300 | 0.1262 | - | - | | -1 | -1 | - | - | 0.9897 (-0.0103) | * The bold row denotes the saved checkpoint. ### Framework Versions - Python: 3.12.12 - Sentence Transformers: 5.2.0 - Transformers: 4.57.3 - PyTorch: 2.9.1+cu128 - Accelerate: 1.12.0 - Datasets: 4.4.2 - 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", } ```