| --- |
| language: |
| - multilingual |
| license: cc-by-nc-4.0 |
| tags: |
| - sentence-transformers |
| - cross-encoder |
| - reranker |
| - generated_from_trainer |
| - dataset_size:13717 |
| - 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 |
| results: |
| - task: |
| type: cross-encoder-reranking |
| name: Cross Encoder Reranking |
| dataset: |
| name: affiliation val |
| type: affiliation-val |
| metrics: |
| - type: map |
| value: 0.9294 |
| name: Map |
| - type: mrr@10 |
| value: 0.9294 |
| name: Mrr@10 |
| - type: ndcg@10 |
| value: 0.9564 |
| name: Ndcg@10 |
| --- |
| |
| # cometadata/jina-reranker-v2-multilingual-affiliations |
|
|
| 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) <!-- at revision 9cfeff2df7d40d1b78e75e5e9cebec92a99813c9 --> |
| - **Maximum Sequence Length:** 1024 tokens |
| - **Number of Output Labels:** 1 label |
| <!-- - **Training Dataset:** Unknown --> |
| - **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") |
| # Get scores for pairs of texts |
| pairs = [ |
| ["Centre sur le handicap et l'intégration, School of Economics and Political Science Université de Saint‐Gall", "College of Saint Benedict and Saint John's University, Collegeville, MN, United States"], |
| ['Swiss Federal Institute of Technology (ETH) Zurich, Institute of Quantum Electronics, Laser Spectroscopy and Sensing Laboratory, Hoenggerberg, HPF D19, CH-8093\u2009Zurich, Switzerland', 'Laboratory of Crystallography, ETH Zurich, CH-8093 Zurich, Switzerland'], |
| ['Swiss Federal Institute of Technology (ETH) Zurich, Institute of Quantum Electronics, Laser Spectroscopy and Sensing Laboratory, Hoenggerberg, HPF D19, CH-8093\u2009Zurich, Switzerland', "Laboratoire d'Electrochimie Physique et Analytique, École Polytechnique Fédérale de Lausanne Station 6, CH-1015 Lausanne, Switzerland"], |
| ['Institute for Advanced Study, Technische Universität München 2 , Lichtenbergstr. 2a, D-85748 Garching, Germany', 'Department of Surgery, Technical University of Munich, School of Medicine, Munich, Germany'], |
| ['Institute for Advanced Study, Technische Universität München 2 , Lichtenbergstr. 2a, D-85748 Garching, Germany', 'Lehrstuhl für BioMolekulare Optik, Ludwig-Maximilians-Universität München, Oettingenstrasse 67, 80538 München (Germany)'], |
| ] |
| scores = model.predict(pairs) |
| print(scores.shape) |
| # (5,) |
| |
| # Or rank different texts based on similarity to a single text |
| ranks = model.rank( |
| "Centre sur le handicap et l'intégration, School of Economics and Political Science Université de Saint‐Gall", |
| [ |
| "College of Saint Benedict and Saint John's University, Collegeville, MN, United States", |
| 'Laboratory of Crystallography, ETH Zurich, CH-8093 Zurich, Switzerland', |
| "Laboratoire d'Electrochimie Physique et Analytique, École Polytechnique Fédérale de Lausanne Station 6, CH-1015 Lausanne, Switzerland", |
| 'Department of Surgery, Technical University of Munich, School of Medicine, Munich, Germany', |
| 'Lehrstuhl für BioMolekulare Optik, Ludwig-Maximilians-Universität München, Oettingenstrasse 67, 80538 München (Germany)', |
| ] |
| ) |
| # [{'corpus_id': ..., 'score': ...}, {'corpus_id': ..., 'score': ...}, ...] |
| ``` |
|
|
| <!-- |
| ### Direct Usage (Transformers) |
|
|
| <details><summary>Click to see the direct usage in Transformers</summary> |
|
|
| </details> |
| --> |
|
|
| <!-- |
| ### Downstream Usage (Sentence Transformers) |
|
|
| You can finetune this model on your own dataset. |
|
|
| <details><summary>Click to expand</summary> |
|
|
| </details> |
| --> |
|
|
| <!-- |
| ### Out-of-Scope Use |
|
|
| *List how the model may foreseeably be misused and address what users ought not to do with the model.* |
| --> |
|
|
| ## Evaluation |
|
|
| ### Metrics |
|
|
| #### Cross Encoder Reranking |
|
|
| * Dataset: `affiliation-val` |
| * Evaluated with [<code>CrossEncoderRerankingEvaluator</code>](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 | 0.9294 (-0.0706) | |
| | mrr@10 | 0.9294 (-0.0706) | |
| | **ndcg@10** | **0.9564 (-0.0436)** | |
|
|
| <!-- |
| ## Bias, Risks and Limitations |
|
|
| *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
| --> |
|
|
| <!-- |
| ### Recommendations |
|
|
| *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
| --> |
|
|
| ## Training Details |
|
|
| ### Training Dataset |
|
|
| #### Unnamed Dataset |
|
|
| * Size: 13,717 training samples |
| * Columns: <code>query</code>, <code>document</code>, and <code>label</code> |
| * Approximate statistics based on the first 1000 samples: |
| | | query | document | label | |
| |:--------|:-----------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------|:------------------------------------------------| |
| | type | string | string | int | |
| | details | <ul><li>min: 6 characters</li><li>mean: 86.53 characters</li><li>max: 273 characters</li></ul> | <ul><li>min: 8 characters</li><li>mean: 88.5 characters</li><li>max: 509 characters</li></ul> | <ul><li>0: ~50.00%</li><li>1: ~50.00%</li></ul> | |
| * Samples: |
| | query | document | label | |
| |:-----------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------|:---------------| |
| | <code>Department of Otolaryngology-Head and Neck Surgery; National Defense Medical College; Saitama Japan</code> | <code>. Department of Otolaryngology-Head and Neck Surgery, National Defense Medical College, Japan.</code> | <code>1</code> | |
| | <code>Department of Otolaryngology-Head and Neck Surgery; National Defense Medical College; Saitama Japan</code> | <code>EOG Resources, Inc</code> | <code>0</code> | |
| | <code>School of Science and Engineering The Chinese University of Hong Kong,Shenzhen,China</code> | <code>School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, China,</code> | <code>1</code> | |
| * Loss: [<code>BinaryCrossEntropyLoss</code>](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: 2,421 evaluation samples |
| * Columns: <code>query</code>, <code>document</code>, and <code>label</code> |
| * Approximate statistics based on the first 1000 samples: |
| | | query | document | label | |
| |:--------|:-------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------|:------------------------------------------------| |
| | type | string | string | int | |
| | details | <ul><li>min: 10 characters</li><li>mean: 100.92 characters</li><li>max: 508 characters</li></ul> | <ul><li>min: 5 characters</li><li>mean: 103.02 characters</li><li>max: 504 characters</li></ul> | <ul><li>0: ~50.00%</li><li>1: ~50.00%</li></ul> | |
| * Samples: |
| | query | document | label | |
| |:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------|:---------------| |
| | <code>Centre sur le handicap et l'intégration, School of Economics and Political Science Université de Saint‐Gall</code> | <code>College of Saint Benedict and Saint John's University, Collegeville, MN, United States</code> | <code>0</code> | |
| | <code>Swiss Federal Institute of Technology (ETH) Zurich, Institute of Quantum Electronics, Laser Spectroscopy and Sensing Laboratory, Hoenggerberg, HPF D19, CH-8093 Zurich, Switzerland</code> | <code>Laboratory of Crystallography, ETH Zurich, CH-8093 Zurich, Switzerland</code> | <code>1</code> | |
| | <code>Swiss Federal Institute of Technology (ETH) Zurich, Institute of Quantum Electronics, Laser Spectroscopy and Sensing Laboratory, Hoenggerberg, HPF D19, CH-8093 Zurich, Switzerland</code> | <code>Laboratoire d'Electrochimie Physique et Analytique, École Polytechnique Fédérale de Lausanne Station 6, CH-1015 Lausanne, Switzerland</code> | <code>0</code> | |
| * Loss: [<code>BinaryCrossEntropyLoss</code>](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 |
| - `learning_rate`: 2e-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 |
|
|
| #### All Hyperparameters |
| <details><summary>Click to expand</summary> |
|
|
| - `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`: 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 |
| - `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`: {} |
|
|
| </details> |
|
|
| ### Training Logs |
| | Epoch | Step | Training Loss | Validation Loss | affiliation-val_ndcg@10 | |
| |:----------:|:--------:|:-------------:|:---------------:|:-----------------------:| |
| | -1 | -1 | - | - | 0.8997 (-0.1003) | |
| | 0.0012 | 1 | 0.0941 | - | - | |
| | 0.1166 | 100 | 0.3775 | - | - | |
| | 0.2331 | 200 | 0.2667 | - | - | |
| | 0.3497 | 300 | 0.2155 | - | - | |
| | 0.4662 | 400 | 0.212 | - | - | |
| | 0.5828 | 500 | 0.2277 | 0.6306 | 0.9465 (-0.0535) | |
| | 0.6993 | 600 | 0.2825 | - | - | |
| | 0.8159 | 700 | 0.2932 | - | - | |
| | 0.9324 | 800 | 0.3123 | - | - | |
| | 1.0490 | 900 | 0.2608 | - | - | |
| | 1.1655 | 1000 | 0.0833 | 0.5776 | 0.9543 (-0.0457) | |
| | 1.2821 | 1100 | 0.0938 | - | - | |
| | 1.3986 | 1200 | 0.1492 | - | - | |
| | 1.5152 | 1300 | 0.1651 | - | - | |
| | 1.6317 | 1400 | 0.1842 | - | - | |
| | 1.7483 | 1500 | 0.2407 | 0.5891 | 0.9555 (-0.0445) | |
| | 1.8648 | 1600 | 0.288 | - | - | |
| | 1.9814 | 1700 | 0.3352 | - | - | |
| | 2.0979 | 1800 | 0.1082 | - | - | |
| | 2.2145 | 1900 | 0.0758 | - | - | |
| | 2.3310 | 2000 | 0.1072 | 0.5725 | 0.9563 (-0.0437) | |
| | 2.4476 | 2100 | 0.1437 | - | - | |
| | 2.5641 | 2200 | 0.153 | - | - | |
| | 2.6807 | 2300 | 0.2176 | - | - | |
| | 2.7972 | 2400 | 0.2513 | - | - | |
| | **2.9138** | **2500** | **0.2949** | **0.5721** | **0.9564 (-0.0436)** | |
| | -1 | -1 | - | - | 0.9564 (-0.0436) | |
| |
| * 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", |
| } |
| ``` |
| |
| <!-- |
| ## Glossary |
| |
| *Clearly define terms in order to be accessible across audiences.* |
| --> |
| |
| <!-- |
| ## Model Card Authors |
| |
| *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.* |
| --> |
| |
| <!-- |
| ## Model Card Contact |
| |
| *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.* |
| --> |
