Add new CrossEncoder model

README.md

@@ -0,0 +1,426 @@
+---
+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': ...}, ...]
+```
+
+<!--
+### 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         | 0.9294 (-0.0706)     |
+| mrr@10      | 0.9294 (-0.0706)     |
+| **ndcg@10** | **0.9564 (-0.0436)** |
+
+<!--
+## 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: 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",
+}
+```
+
+<!--
+## 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.*
+-->

model.safetensors

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