Add new CrossEncoder model

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language:
  - multilingual
license: apache-2.0
tags:
  - sentence-transformers
  - cross-encoder
  - reranker
  - generated_from_trainer
  - dataset_size:16862
  - loss:BinaryCrossEntropyLoss
base_model: Alibaba-NLP/gte-multilingual-reranker-base
pipeline_tag: text-ranking
library_name: sentence-transformers
metrics:
  - map
  - mrr@10
  - ndcg@10
model-index:
  - name: cometadata/gte-multilingual-reranker-affiliations
    results:
      - task:
          type: cross-encoder-reranking
          name: Cross Encoder Reranking
        dataset:
          name: affiliation val
          type: affiliation-val
        metrics:
          - type: map
            value: 0.9666
            name: Map
          - type: mrr@10
            value: 0.9666
            name: Mrr@10
          - type: ndcg@10
            value: 0.9753
            name: Ndcg@10

cometadata/gte-multilingual-reranker-affiliations

This is a Cross Encoder model finetuned from Alibaba-NLP/gte-multilingual-reranker-base 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: Alibaba-NLP/gte-multilingual-reranker-base
Maximum Sequence Length: 8192 tokens
Number of Output Labels: 1 label
Language: multilingual
License: apache-2.0

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/gte-multilingual-reranker-affiliations")
# Get scores for pairs of texts
pairs = [
    ['Université Toulouse', 'a  Université de Toulouse, Mines Albi, CNRS, Centre RAPSODEE ,  Albi ,  France'],
    ['Université Toulouse', 'National Polytechnic Institute of Toulouse'],
    ['School of Fundamental Science and Technology, Keio University 1 , 3-14-1 Hiyoshi, Kohoku-ku, Yokohama 223-8522, Japan', 'Center for Supercentenarian Research, Keio University, Tokyo, Japan'],
    ['School of Fundamental Science and Technology, Keio University 1 , 3-14-1 Hiyoshi, Kohoku-ku, Yokohama 223-8522, Japan', 'g    Toin Human Science and Technology Center, Department of Materials Science and Technology, Toin University of Yokohama, 1614 Kurogane-cho, Aoba-ku, Yokohama 225, Japan'],
    ['Division of Pulmonary and Critical Care Medicine, University of North Carolina School of Medicine, Chapel Hill, North Carolina', 'Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, 101 Manning Drive, CB# 7295, Chapel Hill, NC 27599, USA'],
]
scores = model.predict(pairs)
print(scores.shape)
# (5,)

# Or rank different texts based on similarity to a single text
ranks = model.rank(
    'Université Toulouse',
    [
        'a  Université de Toulouse, Mines Albi, CNRS, Centre RAPSODEE ,  Albi ,  France',
        'National Polytechnic Institute of Toulouse',
        'Center for Supercentenarian Research, Keio University, Tokyo, Japan',
        'g    Toin Human Science and Technology Center, Department of Materials Science and Technology, Toin University of Yokohama, 1614 Kurogane-cho, Aoba-ku, Yokohama 225, Japan',
        'Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, 101 Manning Drive, CB# 7295, Chapel Hill, NC 27599, USA',
    ]
)
# [{'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	0.9666 (-0.0334)
mrr@10	0.9666 (-0.0334)
ndcg@10	0.9753 (-0.0247)

Training Details

Training Dataset

Unnamed Dataset

Size: 16,862 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: 95.73 characters max: 505 characters	min: 8 characters mean: 92.11 characters max: 393 characters	0: ~50.00% 1: ~50.00%

Samples:

query	document	label
`Nanjing University of Science and Technology,Computer Science and Engineering,Nanjing,China`	`Nanjing University of Science And Technology, China`	`1`
`Nanjing University of Science and Technology,Computer Science and Engineering,Nanjing,China`	`Nanjing university of finance & economics, China.`	`0`
`University of Bonn, Bonn, Germany`	`Department of Geophysics, University of Bonn, 53115 Bonn, Germany`	`1`

Loss: BinaryCrossEntropyLoss with these parameters:

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

Evaluation Dataset

Unnamed Dataset

Size: 808 evaluation samples
Columns: query, document, and label

Approximate statistics based on the first 808 samples:

	query	document	label
type	string	string	int
details	min: 14 characters mean: 80.47 characters max: 394 characters	min: 15 characters mean: 109.87 characters max: 500 characters	0: ~50.00% 1: ~50.00%

Samples:

query	document	label
`Université Toulouse`	`a Université de Toulouse, Mines Albi, CNRS, Centre RAPSODEE , Albi , France`	`1`
`Université Toulouse`	`National Polytechnic Institute of Toulouse`	`0`
`School of Fundamental Science and Technology, Keio University 1 , 3-14-1 Hiyoshi, Kohoku-ku, Yokohama 223-8522, Japan`	`Center for Supercentenarian Research, Keio University, Tokyo, Japan`	`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: 32
per_device_eval_batch_size: 32
learning_rate: 2e-05
num_train_epochs: 2
warmup_ratio: 0.1
load_best_model_at_end: True
hub_model_id: cometadata/gte-multilingual-reranker-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: 32
per_device_eval_batch_size: 32
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: 2
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: False
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: False
resume_from_checkpoint: None
hub_model_id: cometadata/gte-multilingual-reranker-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
batch_sampler: batch_sampler
multi_dataset_batch_sampler: proportional
router_mapping: {}
learning_rate_mapping: {}

Training Logs

Epoch	Step	Training Loss	Validation Loss	affiliation-val_ndcg@10
-1	-1	-	-	0.8392 (-0.1608)
0.0019	1	0.6145	-	-
0.1898	100	0.4534	-	-
0.3795	200	0.2997	-	-
0.5693	300	0.2428	-	-
0.7590	400	0.2213	-	-
0.9488	500	0.2311	0.4316	0.9653 (-0.0347)
1.1385	600	0.162	-	-
1.3283	700	0.167	-	-
1.5180	800	0.1712	-	-
1.7078	900	0.1617	-	-
1.8975	1000	0.1511	0.4495	0.9753 (-0.0247)
-1	-1	-	-	0.9753 (-0.0247)

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",
}