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README.md

@@ -1,3 +1,499 @@
----
-license: mit
----
+---
+language:
+- en
+tags:
+- sentence-transformers
+- cross-encoder
+- reranker
+- generated_from_trainer
+- dataset_size:942069
+- loss:PrecomputedDistillationLoss
+base_model: jhu-clsp/ettin-encoder-17m
+datasets:
+- dleemiller/all-nli-distill
+pipeline_tag: text-classification
+library_name: sentence-transformers
+metrics:
+- f1_macro
+- f1_micro
+- f1_weighted
+model-index:
+- name: CrossEncoder based on jhu-clsp/ettin-encoder-17m
+  results:
+  - task:
+      type: cross-encoder-classification
+      name: Cross Encoder Classification
+    dataset:
+      name: AllNLI dev
+      type: AllNLI-dev
+    metrics:
+    - type: f1_macro
+      value: 0.8355168437553738
+      name: F1 Macro
+    - type: f1_micro
+      value: 0.8359363076766546
+      name: F1 Micro
+    - type: f1_weighted
+      value: 0.8361547068271069
+      name: F1 Weighted
+  - task:
+      type: cross-encoder-classification
+      name: Cross Encoder Classification
+    dataset:
+      name: AllNLI test
+      type: AllNLI-test
+    metrics:
+    - type: f1_macro
+      value: 0.8410958360043347
+      name: F1 Macro
+    - type: f1_micro
+      value: 0.8415242165242165
+      name: F1 Micro
+    - type: f1_weighted
+      value: 0.8417682261741827
+      name: F1 Weighted
+---
+
+# CrossEncoder based on jhu-clsp/ettin-encoder-17m
+
+This is a [Cross Encoder](https://www.sbert.net/docs/cross_encoder/usage/usage.html) model finetuned from [jhu-clsp/ettin-encoder-17m](https://huggingface.co/jhu-clsp/ettin-encoder-17m) on the [all-nli-distill](https://huggingface.co/datasets/dleemiller/all-nli-distill) dataset using the [sentence-transformers](https://www.SBERT.net) library. It computes scores for pairs of texts, which can be used for text pair classification.
+
+## Model Details
+
+### Model Description
+- **Model Type:** Cross Encoder
+- **Base model:** [jhu-clsp/ettin-encoder-17m](https://huggingface.co/jhu-clsp/ettin-encoder-17m) <!-- at revision 987607455c61e7a5bbc85f7758e0512ea6d0ae4c -->
+- **Maximum Sequence Length:** 7999 tokens
+- **Number of Output Labels:** 3 labels
+- **Training Dataset:**
+    - [all-nli-distill](https://huggingface.co/datasets/dleemiller/all-nli-distill)
+- **Language:** en
+<!-- - **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 = [
+    ['Two women are embracing while holding to go packages.', 'The sisters are hugging goodbye while holding to go packages after just eating lunch.'],
+    ['Two women are embracing while holding to go packages.', 'Two woman are holding packages.'],
+    ['Two women are embracing while holding to go packages.', 'The men are fighting outside a deli.'],
+    ['Two young children in blue jerseys, one with the number 9 and one with the number 2 are standing on wooden steps in a bathroom and washing their hands in a sink.', 'Two kids in numbered jerseys wash their hands.'],
+    ['Two young children in blue jerseys, one with the number 9 and one with the number 2 are standing on wooden steps in a bathroom and washing their hands in a sink.', 'Two kids at a ballgame wash their hands.'],
+]
+scores = model.predict(pairs)
+print(scores.shape)
+# (5, 3)
+```
+
+<!--
+### 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 Classification
+
+* Datasets: `AllNLI-dev` and `AllNLI-test`
+* Evaluated with [<code>CrossEncoderClassificationEvaluator</code>](https://sbert.net/docs/package_reference/cross_encoder/evaluation.html#sentence_transformers.cross_encoder.evaluation.CrossEncoderClassificationEvaluator)
+
+| Metric       | AllNLI-dev | AllNLI-test |
+|:-------------|:-----------|:------------|
+| **f1_macro** | **0.8355** | **0.8411**  |
+| f1_micro     | 0.8359     | 0.8415      |
+| f1_weighted  | 0.8362     | 0.8418      |
+
+<!--
+## 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
+
+#### all-nli-distill
+
+* Dataset: [all-nli-distill](https://huggingface.co/datasets/dleemiller/all-nli-distill) at [6907d07](https://huggingface.co/datasets/dleemiller/all-nli-distill/tree/6907d071937601df154a4641e824cbce44e8fd41)
+* Size: 942,069 training samples
+* Columns: <code>premise</code>, <code>hypothesis</code>, <code>label</code>, and <code>hash</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | premise                                                                                        | hypothesis                                                                                     | label                                                              | hash                                                                                          |
+  |:--------|:-----------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------|:-------------------------------------------------------------------|:----------------------------------------------------------------------------------------------|
+  | type    | string                                                                                         | string                                                                                         | int                                                                | string                                                                                        |
+  | details | <ul><li>min: 7 characters</li><li>mean: 87.47 characters</li><li>max: 485 characters</li></ul> | <ul><li>min: 3 characters</li><li>mean: 45.98 characters</li><li>max: 157 characters</li></ul> | <ul><li>0: ~32.70%</li><li>1: ~34.20%</li><li>2: ~33.10%</li></ul> | <ul><li>min: 32 characters</li><li>mean: 32.0 characters</li><li>max: 32 characters</li></ul> |
+* Samples:
+  | premise                                                                               | hypothesis                             | label          | hash                                          |
+  |:--------------------------------------------------------------------------------------|:---------------------------------------|:---------------|:----------------------------------------------|
+  | <code>somehow, somewhere.</code>                                                      | <code>Someplace, in some way.</code>   | <code>1</code> | <code>9a14d41bdf965ed999446ea11dbf5b67</code> |
+  | <code>A boy is sitting on a boat with two flags.</code>                               | <code>A blonde person sitting.</code>  | <code>2</code> | <code>758664a444dd4c02d89220da2ab499ac</code> |
+  | <code>A asian male suit clad, uses a umbrella to shield himself from the rain.</code> | <code>He is late for a meeting.</code> | <code>2</code> | <code>7e1155728f9cf33655076ec6b36cdb10</code> |
+* Loss: <code>__main__.PrecomputedDistillationLoss</code>
+
+### Evaluation Dataset
+
+#### all-nli-distill
+
+* Dataset: [all-nli-distill](https://huggingface.co/datasets/dleemiller/all-nli-distill) at [6907d07](https://huggingface.co/datasets/dleemiller/all-nli-distill/tree/6907d071937601df154a4641e824cbce44e8fd41)
+* Size: 19,657 evaluation samples
+* Columns: <code>premise</code>, <code>hypothesis</code>, <code>label</code>, and <code>hash</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | premise                                                                                         | hypothesis                                                                                      | label                                                              | hash                                                                                          |
+  |:--------|:------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------|:----------------------------------------------------------------------------------------------|
+  | type    | string                                                                                          | string                                                                                          | int                                                                | string                                                                                        |
+  | details | <ul><li>min: 16 characters</li><li>mean: 75.01 characters</li><li>max: 229 characters</li></ul> | <ul><li>min: 11 characters</li><li>mean: 37.66 characters</li><li>max: 116 characters</li></ul> | <ul><li>0: ~33.60%</li><li>1: ~33.10%</li><li>2: ~33.30%</li></ul> | <ul><li>min: 32 characters</li><li>mean: 32.0 characters</li><li>max: 32 characters</li></ul> |
+* Samples:
+  | premise                                                            | hypothesis                                                                                         | label          | hash                                          |
+  |:-------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------|:---------------|:----------------------------------------------|
+  | <code>Two women are embracing while holding to go packages.</code> | <code>The sisters are hugging goodbye while holding to go packages after just eating lunch.</code> | <code>2</code> | <code>ee3806dad2b757a8e131aa50f2b73ec9</code> |
+  | <code>Two women are embracing while holding to go packages.</code> | <code>Two woman are holding packages.</code>                                                       | <code>1</code> | <code>563afee877ed42f33dafe7c76fe9604b</code> |
+  | <code>Two women are embracing while holding to go packages.</code> | <code>The men are fighting outside a deli.</code>                                                  | <code>0</code> | <code>fd7c1382a8321094d60105ff37c038da</code> |
+* Loss: <code>__main__.PrecomputedDistillationLoss</code>
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: steps
+- `per_device_train_batch_size`: 512
+- `per_device_eval_batch_size`: 512
+- `learning_rate`: 0.0001
+- `num_train_epochs`: 6
+- `warmup_ratio`: 0.1
+- `bf16`: 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`: 512
+- `per_device_eval_batch_size`: 512
+- `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`: 0.0001
+- `weight_decay`: 0.0
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 1e-08
+- `max_grad_norm`: 1.0
+- `num_train_epochs`: 6
+- `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`: 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`: 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}
+- `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`: 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
+- `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
+<details><summary>Click to expand</summary>
+
+| Epoch  | Step  | Training Loss | Validation Loss | AllNLI-dev_f1_macro | AllNLI-test_f1_macro |
+|:------:|:-----:|:-------------:|:---------------:|:-------------------:|:--------------------:|
+| -1     | -1    | -             | -               | 0.2782              | -                    |
+| 0.0543 | 100   | 6.7398        | -               | -                   | -                    |
+| 0.1087 | 200   | 4.797         | -               | -                   | -                    |
+| 0.1630 | 300   | 3.277         | -               | -                   | -                    |
+| 0.2174 | 400   | 2.8003        | -               | -                   | -                    |
+| 0.2717 | 500   | 2.5854        | 2.5302          | 0.7257              | -                    |
+| 0.3261 | 600   | 2.3873        | -               | -                   | -                    |
+| 0.3804 | 700   | 2.238         | -               | -                   | -                    |
+| 0.4348 | 800   | 2.1506        | -               | -                   | -                    |
+| 0.4891 | 900   | 2.0302        | -               | -                   | -                    |
+| 0.5435 | 1000  | 1.9461        | 1.9746          | 0.7766              | -                    |
+| 0.5978 | 1100  | 1.8948        | -               | -                   | -                    |
+| 0.6522 | 1200  | 1.8219        | -               | -                   | -                    |
+| 0.7065 | 1300  | 1.7425        | -               | -                   | -                    |
+| 0.7609 | 1400  | 1.6744        | -               | -                   | -                    |
+| 0.8152 | 1500  | 1.6372        | 1.6647          | 0.8001              | -                    |
+| 0.8696 | 1600  | 1.605         | -               | -                   | -                    |
+| 0.9239 | 1700  | 1.5654        | -               | -                   | -                    |
+| 0.9783 | 1800  | 1.5148        | -               | -                   | -                    |
+| 1.0326 | 1900  | 1.4017        | -               | -                   | -                    |
+| 1.0870 | 2000  | 1.3142        | 1.5475          | 0.8060              | -                    |
+| 1.1413 | 2100  | 1.2858        | -               | -                   | -                    |
+| 1.1957 | 2200  | 1.2796        | -               | -                   | -                    |
+| 1.25   | 2300  | 1.2624        | -               | -                   | -                    |
+| 1.3043 | 2400  | 1.2757        | -               | -                   | -                    |
+| 1.3587 | 2500  | 1.2399        | 1.4343          | 0.8170              | -                    |
+| 1.4130 | 2600  | 1.25          | -               | -                   | -                    |
+| 1.4674 | 2700  | 1.2519        | -               | -                   | -                    |
+| 1.5217 | 2800  | 1.2179        | -               | -                   | -                    |
+| 1.5761 | 2900  | 1.2035        | -               | -                   | -                    |
+| 1.6304 | 3000  | 1.2185        | 1.3897          | 0.8223              | -                    |
+| 1.6848 | 3100  | 1.1846        | -               | -                   | -                    |
+| 1.7391 | 3200  | 1.1885        | -               | -                   | -                    |
+| 1.7935 | 3300  | 1.1544        | -               | -                   | -                    |
+| 1.8478 | 3400  | 1.1699        | -               | -                   | -                    |
+| 1.9022 | 3500  | 1.1654        | 1.3178          | 0.8279              | -                    |
+| 1.9565 | 3600  | 1.1577        | -               | -                   | -                    |
+| 2.0109 | 3700  | 1.1           | -               | -                   | -                    |
+| 2.0652 | 3800  | 0.8862        | -               | -                   | -                    |
+| 2.1196 | 3900  | 0.8853        | -               | -                   | -                    |
+| 2.1739 | 4000  | 0.8899        | 1.3069          | 0.8282              | -                    |
+| 2.2283 | 4100  | 0.8951        | -               | -                   | -                    |
+| 2.2826 | 4200  | 0.8869        | -               | -                   | -                    |
+| 2.3370 | 4300  | 0.8773        | -               | -                   | -                    |
+| 2.3913 | 4400  | 0.8986        | -               | -                   | -                    |
+| 2.4457 | 4500  | 0.8936        | 1.3049          | 0.8314              | -                    |
+| 2.5    | 4600  | 0.8827        | -               | -                   | -                    |
+| 2.5543 | 4700  | 0.9018        | -               | -                   | -                    |
+| 2.6087 | 4800  | 0.8841        | -               | -                   | -                    |
+| 2.6630 | 4900  | 0.8909        | -               | -                   | -                    |
+| 2.7174 | 5000  | 0.8971        | 1.2616          | 0.8318              | -                    |
+| 2.7717 | 5100  | 0.8851        | -               | -                   | -                    |
+| 2.8261 | 5200  | 0.8795        | -               | -                   | -                    |
+| 2.8804 | 5300  | 0.8793        | -               | -                   | -                    |
+| 2.9348 | 5400  | 0.8827        | -               | -                   | -                    |
+| 2.9891 | 5500  | 0.867         | 1.2425          | 0.8338              | -                    |
+| 3.0435 | 5600  | 0.7091        | -               | -                   | -                    |
+| 3.0978 | 5700  | 0.664         | -               | -                   | -                    |
+| 3.1522 | 5800  | 0.6576        | -               | -                   | -                    |
+| 3.2065 | 5900  | 0.6732        | -               | -                   | -                    |
+| 3.2609 | 6000  | 0.6755        | 1.2826          | 0.8342              | -                    |
+| 3.3152 | 6100  | 0.6762        | -               | -                   | -                    |
+| 3.3696 | 6200  | 0.6677        | -               | -                   | -                    |
+| 3.4239 | 6300  | 0.6869        | -               | -                   | -                    |
+| 3.4783 | 6400  | 0.6807        | -               | -                   | -                    |
+| 3.5326 | 6500  | 0.6759        | 1.2734          | 0.8336              | -                    |
+| 3.5870 | 6600  | 0.6781        | -               | -                   | -                    |
+| 3.6413 | 6700  | 0.678         | -               | -                   | -                    |
+| 3.6957 | 6800  | 0.678         | -               | -                   | -                    |
+| 3.75   | 6900  | 0.6766        | -               | -                   | -                    |
+| 3.8043 | 7000  | 0.6765        | 1.2607          | 0.8362              | -                    |
+| 3.8587 | 7100  | 0.6706        | -               | -                   | -                    |
+| 3.9130 | 7200  | 0.6811        | -               | -                   | -                    |
+| 3.9674 | 7300  | 0.6714        | -               | -                   | -                    |
+| 4.0217 | 7400  | 0.6232        | -               | -                   | -                    |
+| 4.0761 | 7500  | 0.5231        | 1.2781          | 0.8358              | -                    |
+| 4.1304 | 7600  | 0.529         | -               | -                   | -                    |
+| 4.1848 | 7700  | 0.526         | -               | -                   | -                    |
+| 4.2391 | 7800  | 0.5348        | -               | -                   | -                    |
+| 4.2935 | 7900  | 0.5381        | -               | -                   | -                    |
+| 4.3478 | 8000  | 0.5309        | 1.2760          | 0.8363              | -                    |
+| 4.4022 | 8100  | 0.5401        | -               | -                   | -                    |
+| 4.4565 | 8200  | 0.5323        | -               | -                   | -                    |
+| 4.5109 | 8300  | 0.5391        | -               | -                   | -                    |
+| 4.5652 | 8400  | 0.5409        | -               | -                   | -                    |
+| 4.6196 | 8500  | 0.5389        | 1.2844          | 0.8377              | -                    |
+| 4.6739 | 8600  | 0.542         | -               | -                   | -                    |
+| 4.7283 | 8700  | 0.5388        | -               | -                   | -                    |
+| 4.7826 | 8800  | 0.5289        | -               | -                   | -                    |
+| 4.8370 | 8900  | 0.5327        | -               | -                   | -                    |
+| 4.8913 | 9000  | 0.5323        | 1.2743          | 0.8350              | -                    |
+| 4.9457 | 9100  | 0.5326        | -               | -                   | -                    |
+| 5.0    | 9200  | 0.5358        | -               | -                   | -                    |
+| 5.0543 | 9300  | 0.4469        | -               | -                   | -                    |
+| 5.1087 | 9400  | 0.4526        | -               | -                   | -                    |
+| 5.1630 | 9500  | 0.4461        | 1.2887          | 0.8360              | -                    |
+| 5.2174 | 9600  | 0.4476        | -               | -                   | -                    |
+| 5.2717 | 9700  | 0.4442        | -               | -                   | -                    |
+| 5.3261 | 9800  | 0.4508        | -               | -                   | -                    |
+| 5.3804 | 9900  | 0.4456        | -               | -                   | -                    |
+| 5.4348 | 10000 | 0.4452        | 1.2967          | 0.8353              | -                    |
+| 5.4891 | 10100 | 0.4447        | -               | -                   | -                    |
+| 5.5435 | 10200 | 0.4433        | -               | -                   | -                    |
+| 5.5978 | 10300 | 0.438         | -               | -                   | -                    |
+| 5.6522 | 10400 | 0.4437        | -               | -                   | -                    |
+| 5.7065 | 10500 | 0.4428        | 1.2847          | 0.8365              | -                    |
+| 5.7609 | 10600 | 0.4379        | -               | -                   | -                    |
+| 5.8152 | 10700 | 0.4451        | -               | -                   | -                    |
+| 5.8696 | 10800 | 0.4444        | -               | -                   | -                    |
+| 5.9239 | 10900 | 0.4474        | -               | -                   | -                    |
+| 5.9783 | 11000 | 0.4461        | 1.2833          | 0.8355              | -                    |
+| -1     | -1    | -             | -               | -                   | 0.8411               |
+
+</details>
+
+### Framework Versions
+- Python: 3.12.2
+- Sentence Transformers: 5.1.0
+- Transformers: 4.57.0.dev0
+- PyTorch: 2.8.0+cu128
+- Accelerate: 1.10.1
+- Datasets: 4.0.0
+- 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",
+}
+```
+
+<!--
+## 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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+{
+  "architectures": [
+    "ModernBertForSequenceClassification"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": null,
+  "causal_mask": false,
+  "classifier_activation": "gelu",
+  "classifier_bias": false,
+  "classifier_dropout": 0.0,
+  "classifier_pooling": "mean",
+  "cls_token_id": 50281,
+  "decoder_bias": true,
+  "deterministic_flash_attn": false,
+  "dtype": "float32",
+  "embedding_dropout": 0.0,
+  "eos_token_id": null,
+  "global_attn_every_n_layers": 3,
+  "global_rope_theta": 160000.0,
+  "gradient_checkpointing": false,
+  "hidden_activation": "gelu",
+  "hidden_size": 256,
+  "id2label": {
+    "0": "LABEL_0",
+    "1": "LABEL_1",
+    "2": "LABEL_2"
+  },
+  "initializer_cutoff_factor": 2.0,
+  "initializer_range": 0.02,
+  "intermediate_size": 384,
+  "is_causal": false,
+  "label2id": {
+    "LABEL_0": 0,
+    "LABEL_1": 1,
+    "LABEL_2": 2
+  },
+  "layer_norm_eps": 1e-05,
+  "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": 4,
+  "num_hidden_layers": 7,
+  "pad_token_id": 50283,
+  "position_embedding_type": "sans_pos",
+  "repad_logits_with_grad": false,
+  "sentence_transformers": {
+    "activation_fn": "torch.nn.modules.linear.Identity",
+    "version": "5.1.0"
+  },
+  "sep_token_id": 50282,
+  "sparse_pred_ignore_index": -100,
+  "sparse_prediction": false,
+  "transformers_version": "4.57.0.dev0",
+  "vocab_size": 50368
+}

model.safetensors

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+oid sha256:1c7e15b0abb9b485662648f66654d475f54110093b3039646e2b80164e39fcca
+size 67460764

special_tokens_map.json

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+    "rstrip": false,
+    "single_word": false
+  }
+}

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