---
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)
- **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](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': ...}, ...]
```
## Evaluation
### Metrics
#### Cross Encoder Reranking
* Dataset: `affiliation-val`
* Evaluated with [CrossEncoderRerankingEvaluator](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)** |
## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 13,717 training samples
* Columns: query, document, and label
* Approximate statistics based on the first 1000 samples:
| | query | document | label |
|:--------|:-----------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------|:------------------------------------------------|
| type | string | string | int |
| details | - min: 6 characters
- mean: 86.53 characters
- max: 273 characters
| - min: 8 characters
- mean: 88.5 characters
- max: 509 characters
| |
* Samples:
| query | document | label |
|:-----------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------|:---------------|
| Department of Otolaryngology-Head and Neck Surgery; National Defense Medical College; Saitama Japan | . Department of Otolaryngology-Head and Neck Surgery, National Defense Medical College, Japan. | 1 |
| Department of Otolaryngology-Head and Neck Surgery; National Defense Medical College; Saitama Japan | EOG Resources, Inc | 0 |
| School of Science and Engineering The Chinese University of Hong Kong,Shenzhen,China | School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, China, | 1 |
* Loss: [BinaryCrossEntropyLoss](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: query, document, and label
* Approximate statistics based on the first 1000 samples:
| | query | document | label |
|:--------|:-------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------|:------------------------------------------------|
| type | string | string | int |
| details | - min: 10 characters
- mean: 100.92 characters
- max: 508 characters
| - min: 5 characters
- mean: 103.02 characters
- max: 504 characters
| |
* Samples:
| query | document | label |
|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------|:---------------|
| 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 | 0 |
| Swiss Federal Institute of Technology (ETH) Zurich, Institute of Quantum Electronics, Laser Spectroscopy and Sensing Laboratory, Hoenggerberg, HPF D19, CH-8093 Zurich, Switzerland | Laboratory of Crystallography, ETH Zurich, CH-8093 Zurich, Switzerland | 1 |
| Swiss Federal Institute of Technology (ETH) Zurich, Institute of Quantum Electronics, Laser Spectroscopy and Sensing Laboratory, Hoenggerberg, HPF D19, CH-8093 Zurich, Switzerland | Laboratoire d'Electrochimie Physique et Analytique, École Polytechnique Fédérale de Lausanne Station 6, CH-1015 Lausanne, Switzerland | 0 |
* Loss: [BinaryCrossEntropyLoss](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
Click to expand
- `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`: {}