jina-reranker-v2-multilingual-affiliations-large

cometadata/jina-reranker-v2-multilingual-affiliations-large

This is a Cross Encoder model finetuned from jinaai/jina-reranker-v2-base-multilingual using the sentence-transformers 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 <!-- 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
• Documentation: Cross Encoder Documentation
• Repository: Sentence Transformers on GitHub
• Hugging Face: Cross Encoders on Hugging Face

Usage

Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

pip install -U sentence-transformers

Then you can load this model and run inference.

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': ...}, ...]

<!-- ### 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> with these parameters:

  {
      "at_k": 10,
      "always_rerank_positives": true
  }
  

<!-- ## 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: 170,000 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: 22 characters</li><li>mean: 89.21 characters</li><li>max: 209 characters</li></ul>	<ul><li>min: 25 characters</li><li>mean: 101.13 characters</li><li>max: 279 characters</li></ul>	<ul><li>0: ~50.00%</li><li>1: ~50.00%</li></ul>

• Samples:

  query	document	label
  <code>Max-Planck-Institut für Astronomie, Königgstuhl 17, D-69117 Heidelberg, Germany</code>	<code>Max-Planck-Institute for Astronomy, Königstuhl 17, 69117 Heidelberg, Germany e-mail: [email protected]</code>	<code>1</code>
  <code>Max-Planck-Institut für Astronomie, Königgstuhl 17, D-69117 Heidelberg, Germany</code>	<code>Clinical Trials Center Cardiovascular Research Foundation New York City NY USA</code>	<code>0</code>
  <code>Stowers Institute for Medical Research, 64110, Kansas City, Missouri, USA</code>	<code>Stowers Institute for Medical Research, 1,000 East 50th Street, Kansas City, MO 64110, USA</code>	<code>1</code>

• Loss: <code>BinaryCrossEntropyLoss</code> with these parameters:

  {
      "activation_fn": "torch.nn.modules.linear.Identity",
      "pos_weight": null
  }
  

Evaluation Dataset

Unnamed Dataset

• Size: 30,000 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: 28 characters</li><li>mean: 113.4 characters</li><li>max: 298 characters</li></ul>	<ul><li>min: 24 characters</li><li>mean: 104.23 characters</li><li>max: 272 characters</li></ul>	<ul><li>0: ~50.00%</li><li>1: ~50.00%</li></ul>

• Samples:

  query	document	label
  <code>Instituto Multidisciplinar para el Estudio del Medio ‘Ramón Margalef’, Universidad de Alicante, Alicante, Spain</code>	<code>Departamento de Matemática Aplicada, Universidad de Alicante, San Vicente del Raspeig (Alicante), España</code>	<code>1</code>
  <code>Instituto Multidisciplinar para el Estudio del Medio ‘Ramón Margalef’, Universidad de Alicante, Alicante, Spain</code>	<code>Research Institute of Physics and Aerospace Science, University of Vigo, Vigo, Spain</code>	<code>0</code>
  <code>Departamento de Patologia Básica, Setor de Ciências Biológicas, Universidade Federal do Paraná, 81531-970 Curitiba, PR, Brasil</code>	<code>Laboratory of Hematology, Department of Medical Pathology, Federal University of Paraná, Curitiba, Brazil</code>	<code>1</code>

• Loss: <code>BinaryCrossEntropyLoss</code> with these parameters:

  {
      "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

<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: 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: {}

</details>

Training Logs

• 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

@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",
}

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