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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': ...}, ...]
```
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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 | 0.9294 (-0.0706) |
| mrr@10 | 0.9294 (-0.0706) |
| **ndcg@10** | **0.9564 (-0.0436)** |
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### Recommendations
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## 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",
}
```
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