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README.md

@@ -1,3 +1,356 @@
----
-license: mit
----
+---
+tags:
+- sentence-transformers
+- cross-encoder
+- reranker
+- generated_from_trainer
+- dataset_size:5749
+- loss:BinaryCrossEntropyLoss
+pipeline_tag: text-ranking
+library_name: sentence-transformers
+metrics:
+- pearson
+- spearman
+model-index:
+- name: CrossEncoder
+  results:
+  - task:
+      type: cross-encoder-correlation
+      name: Cross Encoder Correlation
+    dataset:
+      name: sts validation
+      type: sts-validation
+    metrics:
+    - type: pearson
+      value: 0.8763053568934394
+      name: Pearson
+    - type: spearman
+      value: 0.8688596158541986
+      name: Spearman
+---
+
+# CrossEncoder
+
+This is a [Cross Encoder](https://www.sbert.net/docs/cross_encoder/usage/usage.html) model trained 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:** [Unknown](https://huggingface.co/unknown) -->
+- **Maximum Sequence Length:** 512 tokens
+- **Number of Output Labels:** 1 label
+<!-- - **Training Dataset:** Unknown -->
+<!-- - **Language:** Unknown -->
+<!-- - **License:** Unknown -->
+
+### 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("cross_encoder_model_id")
+# Get scores for pairs of texts
+pairs = [
+    ['The little boy is singing and playing the guitar.', 'A baby is playing a guitar.'],
+    ['executive director of the arms control association in washington daryl kimball stated that-- the iaea report is 1 in a series of bad signs. ', 'executive director of the arms control association in washington daryl kimball stated the israeli document could affect the debate over india.'],
+    ['it did not say if the men had been hanged in prison. ', 'dozens of such criminals have been hanged in public.'],
+    ['Child sliding in the snow.', 'Man sleeping on the street.'],
+    ["Your confusion doesn't make me a liar.", "Then your confusion doesn't make me a liar either."],
+]
+scores = model.predict(pairs)
+print(scores.shape)
+# (5,)
+
+# Or rank different texts based on similarity to a single text
+ranks = model.rank(
+    'The little boy is singing and playing the guitar.',
+    [
+        'A baby is playing a guitar.',
+        'executive director of the arms control association in washington daryl kimball stated the israeli document could affect the debate over india.',
+        'dozens of such criminals have been hanged in public.',
+        'Man sleeping on the street.',
+        "Then your confusion doesn't make me a liar either.",
+    ]
+)
+# [{'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 Correlation
+
+* Dataset: `sts-validation`
+* Evaluated with [<code>CECorrelationEvaluator</code>](https://sbert.net/docs/package_reference/cross_encoder/evaluation.html#sentence_transformers.cross_encoder.evaluation.CECorrelationEvaluator)
+
+| Metric       | Value      |
+|:-------------|:-----------|
+| pearson      | 0.8763     |
+| **spearman** | **0.8689** |
+
+<!--
+## 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: 5,749 training samples
+* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | sentence_0                                                                                      | sentence_1                                                                                     | label                                                          |
+  |:--------|:------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------|:---------------------------------------------------------------|
+  | type    | string                                                                                          | string                                                                                         | float                                                          |
+  | details | <ul><li>min: 17 characters</li><li>mean: 56.58 characters</li><li>max: 234 characters</li></ul> | <ul><li>min: 16 characters</li><li>mean: 57.3 characters</li><li>max: 235 characters</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.53</li><li>max: 1.0</li></ul> |
+* Samples:
+  | sentence_0                                                                                                                                                | sentence_1                                                                                                                                                  | label             |
+  |:----------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------|
+  | <code>The little boy is singing and playing the guitar.</code>                                                                                            | <code>A baby is playing a guitar.</code>                                                                                                                    | <code>0.56</code> |
+  | <code>executive director of the arms control association in washington daryl kimball stated that-- the iaea report is 1 in a series of bad signs. </code> | <code>executive director of the arms control association in washington daryl kimball stated the israeli document could affect the debate over india.</code> | <code>0.72</code> |
+  | <code>it did not say if the men had been hanged in prison. </code>                                                                                        | <code>dozens of such criminals have been hanged in public.</code>                                                                                           | <code>0.36</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`: 96
+- `per_device_eval_batch_size`: 96
+- `fp16`: 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`: 96
+- `per_device_eval_batch_size`: 96
+- `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`: 5e-05
+- `weight_decay`: 0.0
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 1e-08
+- `max_grad_norm`: 1
+- `num_train_epochs`: 3
+- `max_steps`: -1
+- `lr_scheduler_type`: linear
+- `lr_scheduler_kwargs`: {}
+- `warmup_ratio`: 0.0
+- `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`: False
+- `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}
+- `tp_size`: 0
+- `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}
+- `deepspeed`: None
+- `label_smoothing_factor`: 0.0
+- `optim`: adamw_torch
+- `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
+- `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`: False
+- `neftune_noise_alpha`: None
+- `optim_target_modules`: None
+- `batch_eval_metrics`: False
+- `eval_on_start`: False
+- `use_liger_kernel`: False
+- `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 | sts-validation_spearman |
+|:------:|:----:|:-----------------------:|
+| 0.3333 | 20   | 0.8638                  |
+| 0.6667 | 40   | 0.8646                  |
+| 1.0    | 60   | 0.8663                  |
+| 1.3333 | 80   | 0.8688                  |
+| 1.6667 | 100  | 0.8687                  |
+| 2.0    | 120  | 0.8689                  |
+
+
+### Framework Versions
+- Python: 3.12.2
+- Sentence Transformers: 5.0.0
+- Transformers: 4.51.3
+- PyTorch: 2.7.1+cu126
+- Accelerate: 1.9.0
+- Datasets: 4.0.0
+- Tokenizers: 0.21.2
+
+## 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.*
+-->

config.json

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+{
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+  ],
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+  "causal_mask": false,
+  "classifier_activation": "gelu",
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+  "hidden_activation": "gelu",
+  "hidden_size": 384,
+  "id2label": {
+    "0": "LABEL_0"
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+  "is_causal": false,
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+  "local_attention": 128,
+  "local_rope_theta": 160000.0,
+  "max_position_embeddings": 7999,
+  "mlp_bias": false,
+  "mlp_dropout": 0.0,
+  "model_type": "modernbert",
+  "norm_bias": false,
+  "norm_eps": 1e-05,
+  "num_attention_heads": 6,
+  "num_hidden_layers": 10,
+  "pad_token_id": 50283,
+  "position_embedding_type": "sans_pos",
+  "repad_logits_with_grad": false,
+  "sentence_transformers": {
+    "activation_fn": "torch.nn.modules.activation.Sigmoid",
+    "version": "5.0.0"
+  },
+  "sep_token_id": 50282,
+  "sparse_pred_ignore_index": -100,
+  "sparse_prediction": false,
+  "torch_dtype": "float32",
+  "transformers_version": "4.51.3",
+  "vocab_size": 50368
+}

eval/CrossEncoderCorrelationEvaluator_sts-test-eval_results.csv

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+-1,-1,0.8400583357108699,0.8449063759878362
+-1,-1,0.8763053568934394,0.8688596158541986

eval/CrossEncoderCorrelationEvaluator_sts-validation-eval_results.csv

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+epoch,steps,Pearson_Correlation,Spearman_Correlation
+-1,-1,0.8786893775398513,0.8754715235067954

model.safetensors

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special_tokens_map.json

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+}

tokenizer_config.json

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