gte-multilingual-reranker-affiliations

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 <!-- at revision 8215cf04918ba6f7b6a62bb44238ce2953d8831c -->
• Maximum Sequence Length: 8192 tokens
• Number of Output Labels: 1 label

<!-- - **Training Dataset:** Unknown -->

• 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': ...}, ...]

<!-- ### Direct Usage (Transformers) <details><summary>Click to see the direct usage in Transformers</summary> </details> --> <!-- ### Downstream Usage (Sentence Transformers) You can finetune this model on your own dataset. <details><summary>Click to expand</summary> </details> --> <!-- ### Out-of-Scope Use *List how the model may foreseeably be misused and address what users ought not to do with the model.* -->

Evaluation

Metrics

Cross Encoder Reranking

• Dataset: affiliation-val
• Evaluated with <code>CrossEncoderRerankingEvaluator</code> with these parameters:

  {
      "at_k": 10,
      "always_rerank_positives": true
  }
  

<!-- ## Bias, Risks and Limitations *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* --> <!-- ### Recommendations *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* -->

Training Details

Training Dataset

Unnamed Dataset

• Size: 16,862 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: 95.73 characters</li><li>max: 505 characters</li></ul>	<ul><li>min: 8 characters</li><li>mean: 92.11 characters</li><li>max: 393 characters</li></ul>	<ul><li>0: ~50.00%</li><li>1: ~50.00%</li></ul>

• Samples:

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

• Loss: <code>BinaryCrossEntropyLoss</code> with these parameters:

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

Evaluation Dataset

Unnamed Dataset

• Size: 808 evaluation samples
• Columns: <code>query</code>, <code>document</code>, and <code>label</code>
• Approximate statistics based on the first 808 samples:

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

• Samples:

  query	document	label
  <code>Université Toulouse</code>	<code>a Université de Toulouse, Mines Albi, CNRS, Centre RAPSODEE , Albi , France</code>	<code>1</code>
  <code>Université Toulouse</code>	<code>National Polytechnic Institute of Toulouse</code>	<code>0</code>
  <code>School of Fundamental Science and Technology, Keio University 1 , 3-14-1 Hiyoshi, Kohoku-ku, Yokohama 223-8522, Japan</code>	<code>Center for Supercentenarian Research, Keio University, Tokyo, Japan</code>	<code>1</code>

• Loss: <code>BinaryCrossEntropyLoss</code> 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

<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: 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: {}

</details>

Training Logs

• 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",
}

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