Add new CrossEncoder model

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,458 @@
+---
+language:
+- multilingual
+- fr
+- de
+- zh
+- ru
+- pl
+- es
+- it
+- ja
+- ar
+- hi
+- pt
+- nl
+license: mit
+tags:
+- sentence-transformers
+- cross-encoder
+- reranker
+- generated_from_trainer
+- dataset_size:9358
+- loss:LambdaLoss
+base_model: jhu-clsp/mmBERT-small
+datasets:
+- Antix5/Product_Similarity_Dataset
+pipeline_tag: text-ranking
+library_name: sentence-transformers
+metrics:
+- map
+- mrr@10
+- ndcg@10
+model-index:
+- name: mmBERT-small reranker (LambdaLoss NDCG2++)
+  results:
+  - task:
+      type: cross-encoder-reranking
+      name: Cross Encoder Reranking
+    dataset:
+      name: rerank
+      type: rerank
+    metrics:
+    - type: map
+      value: 0.9562
+      name: Map
+    - type: mrr@10
+      value: 0.9561
+      name: Mrr@10
+    - type: ndcg@10
+      value: 0.9656
+      name: Ndcg@10
+---
+
+# mmBERT-small reranker (LambdaLoss NDCG2++)
+
+This is a [Cross Encoder](https://www.sbert.net/docs/cross_encoder/usage/usage.html) model finetuned from [jhu-clsp/mmBERT-small](https://huggingface.co/jhu-clsp/mmBERT-small) on the [product_similarity_dataset](https://huggingface.co/datasets/Antix5/Product_Similarity_Dataset) dataset 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:** [jhu-clsp/mmBERT-small](https://huggingface.co/jhu-clsp/mmBERT-small) <!-- at revision 13f0442f54d0a41da1dbd91e129e6c4277f9ab2c -->
+- **Maximum Sequence Length:** 256 tokens
+- **Number of Output Labels:** 1 label
+- **Training Dataset:**
+    - [product_similarity_dataset](https://huggingface.co/datasets/Antix5/Product_Similarity_Dataset)
+- **Languages:** multilingual, fr, de, zh, ru, pl, es, it, ja, ar, hi, pt, nl
+- **License:** mit
+
+### 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/UKPLab/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("Antix5/product-reranker-mmBERT-small")
+# Get scores for pairs of texts
+pairs = [
+    ['How many calories in an egg', 'There are on average between 55 and 80 calories in an egg depending on its size.'],
+    ['How many calories in an egg', 'Egg whites are very low in calories, have no fat, no cholesterol, and are loaded with protein.'],
+    ['How many calories in an egg', 'Most of the calories in an egg come from the yellow yolk in the center.'],
+]
+scores = model.predict(pairs)
+print(scores.shape)
+# (3,)
+
+# Or rank different texts based on similarity to a single text
+ranks = model.rank(
+    'How many calories in an egg',
+    [
+        'There are on average between 55 and 80 calories in an egg depending on its size.',
+        'Egg whites are very low in calories, have no fat, no cholesterol, and are loaded with protein.',
+        'Most of the calories in an egg come from the yellow yolk in the center.',
+    ]
+)
+# [{'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: `rerank`
+* 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": false
+  }
+  ```
+
+| Metric      | Value                |
+|:------------|:---------------------|
+| map         | 0.9562 (-0.0359)     |
+| mrr@10      | 0.9561 (-0.0385)     |
+| **ndcg@10** | **0.9656 (-0.0291)** |
+
+<!--
+## 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
+
+#### product_similarity_dataset
+
+* Dataset: [product_similarity_dataset](https://huggingface.co/datasets/Antix5/Product_Similarity_Dataset) at [7aba3ef](https://huggingface.co/datasets/Antix5/Product_Similarity_Dataset/tree/7aba3ef7a1d4bb067b26c4cafb376f7447850314)
+* Size: 9,358 training samples
+* Columns: <code>query</code>, <code>documents</code>, and <code>scores</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | query                                                                                          | documents                           | scores                              |
+  |:--------|:-----------------------------------------------------------------------------------------------|:------------------------------------|:------------------------------------|
+  | type    | string                                                                                         | list                                | list                                |
+  | details | <ul><li>min: 6 characters</li><li>mean: 57.18 characters</li><li>max: 197 characters</li></ul> | <ul><li>size: 16 elements</li></ul> | <ul><li>size: 16 elements</li></ul> |
+* Samples:
+  | query                                                               | documents                                                                                                                                                                                                                                    | scores                                         |
+  |:--------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------|
+  | <code>Premier 26764 Car Spinner, Santa, 25 by 19-1/2-Inch</code>    | <code>['Premier 26764 Tourbillon pour voiture, Santa, 25 x 19-1/2 pouces', 'BNTS, ЧИПСЫ ИЗ ФАСОЛИ NV И МОРСКАЯ СОЛЬ', 'Beanitos, Чипс из фасоли navy, Сыр на чо', 'K2 स्केट व्हील (4 का पैक)', 'BLST BALL МЯЧ ДЛЯ КИКА (2 ШТ.)', ...]</code> | <code>[1.0, 0.0, 0.0, 0.0, 0.0, ...]</code>    |
+  | <code>Juice Cocktail Blend From Concentrate, Apple Blueberry</code> | <code>['Mélange de cocktail de jus à base de concentré, pomme myrtille', 'Orange Juice From Concentrate With Pulp', 'Tropical Juice Splash From Concentrate', 'BLUEBERRY JUICE DRNK', 'APPLE NECTAR JUICE DRINK FROM CNCNTRT', ...]</code>   | <code>[1.0, 0.4, 0.35, 0.65, 0.55, ...]</code> |
+  | <code>Fruity Sour Strips Fruit-Flavored Chewy Candy</code>          | <code>['Fruity Sour Strips Fruit-Flavored Chewy Candy', 'SR CANDIES, FRUIT SOUR', 'Fruit Candy, Fruit', 'FRT SNCK TUTTI FRUITY', 'Fruit Strips, Peach Passion', ...]</code>                                                                  | <code>[1.0, 0.95, 0.7, 0.55, 0.9, ...]</code>  |
+* Loss: [<code>LambdaLoss</code>](https://sbert.net/docs/package_reference/cross_encoder/losses.html#lambdaloss) with these parameters:
+  ```json
+  {
+      "weighting_scheme": "sentence_transformers.cross_encoder.losses.LambdaLoss.NDCGLoss2PPScheme",
+      "k": null,
+      "sigma": 1.0,
+      "eps": 1e-10,
+      "reduction_log": "binary",
+      "activation_fn": "torch.nn.modules.linear.Identity",
+      "mini_batch_size": null
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: steps
+- `learning_rate`: 2e-05
+- `num_train_epochs`: 1
+- `warmup_ratio`: 0.1
+- `fp16`: True
+- `load_best_model_at_end`: True
+- `gradient_checkpointing`: True
+
+#### 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`: 8
+- `per_device_eval_batch_size`: 8
+- `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`: 1
+- `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
+- `use_ipex`: False
+- `bf16`: False
+- `fp16`: True
+- `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
+- `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`: None
+- `hub_strategy`: every_save
+- `hub_private_repo`: None
+- `hub_always_push`: False
+- `hub_revision`: None
+- `gradient_checkpointing`: True
+- `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`: False
+- `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`: False
+- `prompts`: None
+- `batch_sampler`: batch_sampler
+- `multi_dataset_batch_sampler`: proportional
+- `router_mapping`: {}
+- `learning_rate_mapping`: {}
+
+</details>
+
+### Training Logs
+| Epoch  | Step | Training Loss | rerank_ndcg@10   |
+|:------:|:----:|:-------------:|:----------------:|
+| 0.0009 | 1    | 0.1261        | -                |
+| 0.0171 | 20   | 0.1193        | -                |
+| 0.0342 | 40   | 0.0767        | -                |
+| 0.0513 | 60   | 0.0563        | -                |
+| 0.0684 | 80   | 0.055         | -                |
+| 0.0855 | 100  | 0.0546        | -                |
+| 0.1026 | 120  | 0.0483        | -                |
+| 0.1197 | 140  | 0.0489        | -                |
+| 0.1368 | 160  | 0.049         | -                |
+| 0.1538 | 180  | 0.0463        | -                |
+| 0.1709 | 200  | 0.046         | 0.9419 (-0.0528) |
+| 0.1880 | 220  | 0.0411        | -                |
+| 0.2051 | 240  | 0.0398        | -                |
+| 0.2222 | 260  | 0.0456        | -                |
+| 0.2393 | 280  | 0.0463        | -                |
+| 0.2564 | 300  | 0.043         | -                |
+| 0.2735 | 320  | 0.0447        | -                |
+| 0.2906 | 340  | 0.0419        | -                |
+| 0.3077 | 360  | 0.0403        | -                |
+| 0.3248 | 380  | 0.0429        | -                |
+| 0.3419 | 400  | 0.0423        | 0.9653 (-0.0294) |
+| 0.3590 | 420  | 0.0406        | -                |
+| 0.3761 | 440  | 0.041         | -                |
+| 0.3932 | 460  | 0.0427        | -                |
+| 0.4103 | 480  | 0.0376        | -                |
+| 0.4274 | 500  | 0.0408        | -                |
+| 0.4444 | 520  | 0.0394        | -                |
+| 0.4615 | 540  | 0.0423        | -                |
+| 0.4786 | 560  | 0.0403        | -                |
+| 0.4957 | 580  | 0.0336        | -                |
+| 0.5128 | 600  | 0.039         | 0.9668 (-0.0279) |
+| 0.5299 | 620  | 0.0389        | -                |
+| 0.5470 | 640  | 0.0376        | -                |
+| 0.5641 | 660  | 0.0422        | -                |
+| 0.5812 | 680  | 0.0406        | -                |
+| 0.5983 | 700  | 0.037         | -                |
+| 0.6154 | 720  | 0.0368        | -                |
+| 0.6325 | 740  | 0.0365        | -                |
+| 0.6496 | 760  | 0.0356        | -                |
+| 0.6667 | 780  | 0.0359        | -                |
+| 0.6838 | 800  | 0.0368        | 0.9646 (-0.0301) |
+| 0.7009 | 820  | 0.0342        | -                |
+| 0.7179 | 840  | 0.0376        | -                |
+| 0.7350 | 860  | 0.036         | -                |
+| 0.7521 | 880  | 0.0331        | -                |
+| 0.7692 | 900  | 0.0341        | -                |
+| 0.7863 | 920  | 0.0372        | -                |
+| 0.8034 | 940  | 0.0361        | -                |
+| 0.8205 | 960  | 0.0352        | -                |
+| 0.8376 | 980  | 0.0351        | -                |
+| 0.8547 | 1000 | 0.0348        | 0.9620 (-0.0327) |
+| 0.8718 | 1020 | 0.0341        | -                |
+| 0.8889 | 1040 | 0.0354        | -                |
+| 0.9060 | 1060 | 0.035         | -                |
+| 0.9231 | 1080 | 0.0325        | -                |
+| 0.9402 | 1100 | 0.038         | -                |
+| 0.9573 | 1120 | 0.0376        | -                |
+| 0.9744 | 1140 | 0.0335        | -                |
+| 0.9915 | 1160 | 0.0375        | -                |
+| -1     | -1   | -             | 0.9656 (-0.0291) |
+
+
+### Framework Versions
+- Python: 3.12.11
+- Sentence Transformers: 5.1.0
+- Transformers: 4.56.1
+- PyTorch: 2.8.0+cu126
+- Accelerate: 1.10.1
+- Datasets: 2.20.0
+- Tokenizers: 0.22.0
+
+## 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",
+}
+```
+
+#### LambdaLoss
+```bibtex
+@inproceedings{wang2018lambdaloss,
+  title={The LambdaLoss Framework for Ranking Metric Optimization},
+  author={Wang, Xuanhui and Li, Cheng and Golbandi, Nadav and Bendersky, Michael and Najork, Marc},
+  booktitle={Proceedings of the 27th ACM international conference on information and knowledge management},
+  pages={1313--1322},
+  year={2018}
+}
+```
+
+<!--
+## 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.*
+-->

config.json

@@ -0,0 +1,56 @@
+{
+  "architectures": [
+    "ModernBertForSequenceClassification"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 2,
+  "classifier_activation": "gelu",
+  "classifier_bias": false,
+  "classifier_dropout": 0.0,
+  "classifier_pooling": "mean",
+  "cls_token_id": 1,
+  "decoder_bias": true,
+  "deterministic_flash_attn": false,
+  "dtype": "float32",
+  "embedding_dropout": 0.0,
+  "eos_token_id": 1,
+  "global_attn_every_n_layers": 3,
+  "global_rope_theta": 160000,
+  "gradient_checkpointing": false,
+  "hidden_activation": "gelu",
+  "hidden_size": 384,
+  "id2label": {
+    "0": "LABEL_0"
+  },
+  "initializer_cutoff_factor": 2.0,
+  "initializer_range": 0.02,
+  "intermediate_size": 1152,
+  "label2id": {
+    "LABEL_0": 0
+  },
+  "layer_norm_eps": 1e-05,
+  "local_attention": 128,
+  "local_rope_theta": 160000,
+  "mask_token_id": 4,
+  "max_position_embeddings": 8192,
+  "mlp_bias": false,
+  "mlp_dropout": 0.0,
+  "model_type": "modernbert",
+  "norm_bias": false,
+  "norm_eps": 1e-05,
+  "num_attention_heads": 6,
+  "num_hidden_layers": 22,
+  "pad_token_id": 0,
+  "position_embedding_type": "sans_pos",
+  "repad_logits_with_grad": false,
+  "sentence_transformers": {
+    "activation_fn": "torch.nn.modules.activation.Sigmoid",
+    "version": "5.1.0"
+  },
+  "sep_token_id": 1,
+  "sparse_pred_ignore_index": -100,
+  "sparse_prediction": false,
+  "transformers_version": "4.56.1",
+  "vocab_size": 256000
+}

model.safetensors

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+oid sha256:fe03ead3dacba9e6363b2b1a582798a054aa0381844546fe10f3d789988c8d9d
+size 562581852

special_tokens_map.json

@@ -0,0 +1,55 @@
+{
+  "additional_special_tokens": [
+    "<start_of_turn>",
+    "<end_of_turn>"
+  ],
+  "bos_token": {
+    "content": "<bos>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "cls_token": {
+    "content": "<bos>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "<eos>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "<mask>",
+    "lstrip": true,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<pad>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "<eos>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0a5069feab370ece9f452dcda41c0ba2c216eebf6cb49b06893698f53cdc2b0b
+size 34363441

tokenizer_config.json

@@ -0,0 +1,2024 @@
+{
+  "add_bos_token": true,
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<pad>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "<eos>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "<bos>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
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