End of training

README.md

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+---
+library_name: transformers
+license: mit
+base_model: microsoft/mdeberta-v3-base
+tags:
+- generated_from_trainer
+model-index:
+- name: mdeberta-semeval25_narratives09_fold1
+  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. -->
+
+# mdeberta-semeval25_narratives09_fold1
+
+This model is a fine-tuned version of [microsoft/mdeberta-v3-base](https://huggingface.co/microsoft/mdeberta-v3-base) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 4.1440
+- Precision Samples: 0.3489
+- Recall Samples: 0.7666
+- F1 Samples: 0.4484
+- Precision Macro: 0.6713
+- Recall Macro: 0.4701
+- F1 Macro: 0.2642
+- Precision Micro: 0.3133
+- Recall Micro: 0.7518
+- F1 Micro: 0.4423
+- Precision Weighted: 0.4454
+- Recall Weighted: 0.7518
+- F1 Weighted: 0.3929
+
+## 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: 32
+- 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: 10
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Precision Samples | Recall Samples | F1 Samples | Precision Macro | Recall Macro | F1 Macro | Precision Micro | Recall Micro | F1 Micro | Precision Weighted | Recall Weighted | F1 Weighted |
+|:-------------:|:-----:|:----:|:---------------:|:-----------------:|:--------------:|:----------:|:---------------:|:------------:|:--------:|:---------------:|:------------:|:--------:|:------------------:|:---------------:|:-----------:|
+| 5.3976        | 1.0   | 19   | 5.3094          | 1.0               | 0.0            | 0.0        | 1.0             | 0.0476       | 0.0476   | 1.0             | 0.0          | 0.0      | 1.0                | 0.0             | 0.0         |
+| 5.0729        | 2.0   | 38   | 5.0051          | 0.2991            | 0.4812         | 0.3465     | 0.8683          | 0.2245       | 0.1355   | 0.3056          | 0.4496       | 0.3639   | 0.6682             | 0.4496          | 0.2244      |
+| 4.799         | 3.0   | 57   | 4.7268          | 0.3634            | 0.5035         | 0.3759     | 0.8348          | 0.2364       | 0.1574   | 0.3291          | 0.4640       | 0.3851   | 0.6206             | 0.4640          | 0.2617      |
+| 4.4077        | 4.0   | 76   | 4.5072          | 0.3846            | 0.6225         | 0.4435     | 0.7933          | 0.3190       | 0.2043   | 0.3383          | 0.5755       | 0.4261   | 0.5591             | 0.5755          | 0.3232      |
+| 4.1905        | 5.0   | 95   | 4.3919          | 0.4006            | 0.6444         | 0.4575     | 0.7484          | 0.3320       | 0.2140   | 0.3395          | 0.5935       | 0.4319   | 0.5242             | 0.5935          | 0.3411      |
+| 4.1939        | 6.0   | 114  | 4.2724          | 0.3817            | 0.7296         | 0.4634     | 0.7094          | 0.4205       | 0.2478   | 0.3229          | 0.7050       | 0.4429   | 0.4663             | 0.7050          | 0.3791      |
+| 3.9286        | 7.0   | 133  | 4.2600          | 0.3753            | 0.7336         | 0.4620     | 0.6853          | 0.4257       | 0.2568   | 0.3311          | 0.7050       | 0.4506   | 0.4556             | 0.7050          | 0.3882      |
+| 3.8896        | 8.0   | 152  | 4.1871          | 0.3528            | 0.7581         | 0.4505     | 0.6713          | 0.4559       | 0.2625   | 0.3188          | 0.7374       | 0.4452   | 0.4462             | 0.7374          | 0.3929      |
+| 3.993         | 9.0   | 171  | 4.1598          | 0.3525            | 0.7629         | 0.4503     | 0.6712          | 0.4645       | 0.2639   | 0.3170          | 0.7446       | 0.4447   | 0.4443             | 0.7446          | 0.3920      |
+| 4.1424        | 10.0  | 190  | 4.1440          | 0.3489            | 0.7666         | 0.4484     | 0.6713          | 0.4701       | 0.2642   | 0.3133          | 0.7518       | 0.4423   | 0.4454             | 0.7518          | 0.3929      |
+
+
+### Framework versions
+
+- Transformers 4.46.0
+- Pytorch 2.3.1
+- Datasets 2.21.0
+- Tokenizers 0.20.1

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