End of training

README.md

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+---
+library_name: transformers
+license: mit
+base_model: microsoft/mdeberta-v3-base
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+model-index:
+- name: nli-subgroups-target-abroad
+  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. -->
+
+# nli-subgroups-target-abroad
+
+This model is a fine-tuned version of [microsoft/mdeberta-v3-base](https://huggingface.co/microsoft/mdeberta-v3-base) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 1.3895
+- Accuracy: 0.9270
+- Precision Binary: 0.5491
+- Recall Binary: 0.6333
+- F1 Binary: 0.5882
+- Precision Micro: 0.9270
+- Recall Micro: 0.9270
+- F1 Micro: 0.9270
+- Pr Auc: 0.5737
+- Cohen Kappa: 0.5484
+
+## 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: 16
+- eval_batch_size: 32
+- seed: 42
+- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: linear
+- num_epochs: 3
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision Binary | Recall Binary | F1 Binary | Precision Micro | Recall Micro | F1 Micro | Pr Auc | Cohen Kappa |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:----------------:|:-------------:|:---------:|:---------------:|:------------:|:--------:|:------:|:-----------:|
+| No log        | 1.0   | 456  | 1.0509          | 0.9237   | 0.5307           | 0.6333        | 0.5775    | 0.9237          | 0.9237       | 0.9237   | 0.5397 | 0.5359      |
+| 1.1638        | 2.0   | 912  | 0.7448          | 0.9303   | 0.5782           | 0.5667        | 0.5724    | 0.9303          | 0.9303       | 0.9303   | 0.5361 | 0.5345      |
+| 0.9414        | 3.0   | 1368 | 1.3895          | 0.9270   | 0.5491           | 0.6333        | 0.5882    | 0.9270          | 0.9270       | 0.9270   | 0.5737 | 0.5484      |
+
+
+### Framework versions
+
+- Transformers 4.51.3
+- Pytorch 2.6.0+cu124
+- Datasets 2.14.4
+- Tokenizers 0.21.1