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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) <!-- 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-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': ...}, ...]
```
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</details>
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### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
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### Out-of-Scope Use
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## 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 | 0.9880 (-0.0120) |
| mrr@10 | 0.9880 (-0.0120) |
| **ndcg@10** | **0.9933 (-0.0067)** |
<!--
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### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
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## 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: beuther@mpia.de</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>](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: <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>](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
<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
| 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",
}
```
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