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
• 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
• 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': ...}, ...]

Evaluation

Metrics

Cross Encoder Reranking

• Dataset: affiliation-val
• Evaluated with CrossEncoderRerankingEvaluator with these parameters:

  {
      "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	min: 22 characters mean: 89.21 characters max: 209 characters	min: 25 characters mean: 101.13 characters max: 279 characters	0: ~50.00% 1: ~50.00%

• 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 with these parameters:

  {
      "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	min: 28 characters mean: 113.4 characters max: 298 characters	min: 24 characters mean: 104.23 characters max: 272 characters	0: ~50.00% 1: ~50.00%

• 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 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

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

@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",
}