End of training

README.md

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+---
+library_name: transformers
+license: apache-2.0
+base_model: answerdotai/ModernBERT-large
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+- f1
+model-index:
+- name: USS-reward-model-binary-2vs4
+  results: []
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# USS-reward-model-binary-2vs4
+
+This model is a fine-tuned version of [answerdotai/ModernBERT-large](https://huggingface.co/answerdotai/ModernBERT-large) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.8530
+- Accuracy: 0.8806
+- F1: 0.9333
+- Auc Roc: 0.8771
+- Mcc: 0.3681
+- Confusion Matrix: [[3, 5], [3, 56]]
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 2e-05
+- train_batch_size: 2
+- eval_batch_size: 1
+- seed: 42
+- distributed_type: multi-GPU
+- gradient_accumulation_steps: 10
+- total_train_batch_size: 20
+- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: linear
+- num_epochs: 10
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Auc Roc | Mcc    | Confusion Matrix   |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:-------:|:------:|:------------------:|
+| 4.9132        | 1.0   | 24   | 0.4302          | 0.8806   | 0.9365 | 0.7479  | 0.0    | [[0, 8], [0, 59]]  |
+| 4.2935        | 2.0   | 48   | 0.4398          | 0.8806   | 0.9365 | 0.7839  | 0.0    | [[0, 8], [0, 59]]  |
+| 3.8073        | 3.0   | 72   | 0.2622          | 0.8955   | 0.9421 | 0.8729  | 0.4209 | [[3, 5], [2, 57]]  |
+| 2.1451        | 4.0   | 96   | 0.3419          | 0.9104   | 0.9516 | 0.9121  | 0.4764 | [[2, 6], [0, 59]]  |
+| 0.9600        | 5.0   | 120  | 0.9652          | 0.8209   | 0.8889 | 0.875   | 0.5037 | [[7, 1], [11, 48]] |
+| 0.5732        | 6.0   | 144  | 0.8316          | 0.8657   | 0.9244 | 0.8443  | 0.3257 | [[3, 5], [4, 55]]  |
+| 0.1811        | 7.0   | 168  | 0.9996          | 0.9104   | 0.9516 | 0.8559  | 0.4764 | [[2, 6], [0, 59]]  |
+| 0.2513        | 8.0   | 192  | 0.7501          | 0.8955   | 0.9421 | 0.8729  | 0.4209 | [[3, 5], [2, 57]]  |
+| 0.0001        | 9.0   | 216  | 0.8585          | 0.8955   | 0.9421 | 0.8792  | 0.4209 | [[3, 5], [2, 57]]  |
+| 0.0000        | 10.0  | 240  | 0.8530          | 0.8806   | 0.9333 | 0.8771  | 0.3681 | [[3, 5], [3, 56]]  |
+
+
+### Framework versions
+
+- Transformers 5.9.0
+- Pytorch 2.12.0+cu130
+- Datasets 4.8.5
+- Tokenizers 0.22.2

model.safetensors

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