| | --- |
| | 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](https://www.sbert.net/docs/cross_encoder/usage/usage.html) model finetuned from [Alibaba-NLP/gte-multilingual-reranker-base](https://huggingface.co/Alibaba-NLP/gte-multilingual-reranker-base) 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:** [Alibaba-NLP/gte-multilingual-reranker-base](https://huggingface.co/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](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/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>](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 | 0.9666 (-0.0334) | |
| | | mrr@10 | 0.9666 (-0.0334) | |
| | | **ndcg@10** | **0.9753 (-0.0247)** | |
| |
|
| | <!-- |
| | ## 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>](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: 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>](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`: 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 |
| | | 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 |
| | ```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", |
| | } |
| | ``` |
| | |
| | <!-- |
| | ## Glossary |
| | |
| | *Clearly define terms in order to be accessible across audiences.* |
| | --> |
| | |
| | <!-- |
| | ## Model Card Authors |
| | |
| | *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.* |
| | --> |
| | |
| | <!-- |
| | ## Model Card Contact |
| | |
| | *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.* |
| | --> |
