--- language: - multilingual license: cc-by-nc-4.0 tags: - sentence-transformers - cross-encoder - reranker - generated_from_trainer - dataset_size:170000 - 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: 0.988 name: Map - type: mrr@10 value: 0.988 name: Mrr@10 - type: ndcg@10 value: 0.9933 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 = [ ['Instituto Multidisciplinar para el Estudio del Medio ‘Ramón Margalef’, Universidad de Alicante, Alicante, Spain', 'Departamento de Matemática Aplicada, Universidad de Alicante, San Vicente del Raspeig (Alicante), España'], ['Instituto Multidisciplinar para el Estudio del Medio ‘Ramón Margalef’, Universidad de Alicante, Alicante, Spain', 'Research Institute of Physics and Aerospace Science, University of Vigo, Vigo, Spain'], ['Departamento de Patologia Básica, Setor de Ciências Biológicas, Universidade Federal do Paraná, 81531-970 Curitiba, PR, Brasil', 'Laboratory of Hematology, Department of Medical Pathology, Federal University of Paraná, Curitiba, Brazil'], ['Departamento de Patologia Básica, Setor de Ciências Biológicas, Universidade Federal do Paraná, 81531-970 Curitiba, PR, Brasil', 'Laboratório de Patologia Experimental Pontifícia Universidade Católica do Paraná Curitiba Brazil'], ['Institute of Information & Control, Hangzhou Dianzi University, Hangzhou 310018, P.R. China', 'College of Media & Design Hangzhou Dianzi University Hangzhou 310018 China'], ] scores = model.predict(pairs) print(scores.shape) # (5,) # Or rank different texts based on similarity to a single text ranks = model.rank( 'Instituto Multidisciplinar para el Estudio del Medio ‘Ramón Margalef’, Universidad de Alicante, Alicante, Spain', [ 'Departamento de Matemática Aplicada, Universidad de Alicante, San Vicente del Raspeig (Alicante), España', 'Research Institute of Physics and Aerospace Science, University of Vigo, Vigo, Spain', 'Laboratory of Hematology, Department of Medical Pathology, Federal University of Paraná, Curitiba, Brazil', 'Laboratório de Patologia Experimental Pontifícia Universidade Católica do Paraná Curitiba Brazil', 'College of Media & Design Hangzhou Dianzi University Hangzhou 310018 China', ] ) # [{'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 | 0.9880 (-0.0120) | | mrr@10 | 0.9880 (-0.0120) | | **ndcg@10** | **0.9933 (-0.0067)** | ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 170,000 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 | | Stowers Institute for Medical Research, 64110, Kansas City, Missouri, USA | Stowers Institute for Medical Research, 1,000 East 50th Street, Kansas City, MO 64110, USA | 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: 30,000 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 | |:--------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------|:---------------| | Instituto Multidisciplinar para el Estudio del Medio ‘Ramón Margalef’, Universidad de Alicante, Alicante, Spain | Departamento de Matemática Aplicada, Universidad de Alicante, San Vicente del Raspeig (Alicante), España | 1 | | Instituto Multidisciplinar para el Estudio del Medio ‘Ramón Margalef’, Universidad de Alicante, Alicante, Spain | Research Institute of Physics and Aerospace Science, University of Vigo, Vigo, Spain | 0 | | Departamento de Patologia Básica, Setor de Ciências Biológicas, Universidade Federal do Paraná, 81531-970 Curitiba, PR, Brasil | Laboratory of Hematology, Department of Medical Pathology, Federal University of Paraná, Curitiba, Brazil | 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 | - | - | 0.9200 (-0.0800) | | 0.0008 | 1 | 0.1129 | - | - | | 0.0752 | 100 | 0.3049 | - | - | | 0.1505 | 200 | 0.1295 | - | - | | 0.1881 | 250 | - | 0.6259 | 0.9715 (-0.0285) | | 0.2257 | 300 | 0.1076 | - | - | | 0.3010 | 400 | 0.0978 | - | - | | 0.3762 | 500 | 0.1031 | 0.2822 | 0.9871 (-0.0129) | | 0.4515 | 600 | 0.0932 | - | - | | 0.5267 | 700 | 0.1015 | - | - | | 0.5643 | 750 | - | 0.2395 | 0.9890 (-0.0110) | | 0.6020 | 800 | 0.0999 | - | - | | 0.6772 | 900 | 0.1112 | - | - | | 0.7524 | 1000 | 0.1196 | 0.1980 | 0.9921 (-0.0079) | | 0.8277 | 1100 | 0.1288 | - | - | | 0.9029 | 1200 | 0.1295 | - | - | | 0.9406 | 1250 | - | 0.1773 | 0.9929 (-0.0071) | | 0.9782 | 1300 | 0.1338 | - | - | | 1.0534 | 1400 | 0.0585 | - | - | | 1.1287 | 1500 | 0.0295 | 0.3412 | 0.9879 (-0.0121) | | 1.2039 | 1600 | 0.0412 | - | - | | 1.2792 | 1700 | 0.0491 | - | - | | 1.3168 | 1750 | - | 0.2622 | 0.9903 (-0.0097) | | 1.3544 | 1800 | 0.0619 | - | - | | 1.4296 | 1900 | 0.0612 | - | - | | 1.5049 | 2000 | 0.0676 | 0.2131 | 0.9919 (-0.0081) | | 1.5801 | 2100 | 0.073 | - | - | | 1.6554 | 2200 | 0.0801 | - | - | | 1.6930 | 2250 | - | 0.1940 | 0.9927 (-0.0073) | | 1.7306 | 2300 | 0.0963 | - | - | | 1.8059 | 2400 | 0.1114 | - | - | | **1.8811** | **2500** | **0.1083** | **0.1773** | **0.9933 (-0.0067)** | | 1.9564 | 2600 | 0.1203 | - | - | | 2.0316 | 2700 | 0.0841 | - | - | | 2.0692 | 2750 | - | 0.2898 | 0.9907 (-0.0093) | | 2.1068 | 2800 | 0.0248 | - | - | | 2.1821 | 2900 | 0.032 | - | - | | 2.2573 | 3000 | 0.0468 | 0.2455 | 0.9915 (-0.0085) | | 2.3326 | 3100 | 0.0497 | - | - | | 2.4078 | 3200 | 0.0585 | - | - | | 2.4454 | 3250 | - | 0.2142 | 0.9921 (-0.0079) | | 2.4831 | 3300 | 0.0653 | - | - | | 2.5583 | 3400 | 0.0701 | - | - | | 2.6336 | 3500 | 0.0758 | 0.2034 | 0.9924 (-0.0076) | | 2.7088 | 3600 | 0.0903 | - | - | | 2.7840 | 3700 | 0.1037 | - | - | | 2.8217 | 3750 | - | 0.1984 | 0.9927 (-0.0073) | | 2.8593 | 3800 | 0.113 | - | - | | 2.9345 | 3900 | 0.1199 | - | - | | -1 | -1 | - | - | 0.9933 (-0.0067) | * 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", } ```