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_narratives_fold2
+  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_narratives_fold2
+
+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: 4.2885
+- Precision Samples: 0.3350
+- Recall Samples: 0.7536
+- F1 Samples: 0.4333
+- Precision Macro: 0.6879
+- Recall Macro: 0.4863
+- F1 Macro: 0.2811
+- Precision Micro: 0.3050
+- Recall Micro: 0.7283
+- F1 Micro: 0.4299
+- Precision Weighted: 0.4670
+- Recall Weighted: 0.7283
+- F1 Weighted: 0.3780
+
+## 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 |
+|:-------------:|:-----:|:----:|:---------------:|:-----------------:|:--------------:|:----------:|:---------------:|:------------:|:--------:|:---------------:|:------------:|:--------:|:------------------:|:---------------:|:-----------:|
+| 5.4789        | 1.0   | 19   | 5.4030          | 0.3379            | 0.1101         | 0.1439     | 0.9654          | 0.0927       | 0.0678   | 0.2727          | 0.1304       | 0.1765   | 0.8999             | 0.1304          | 0.0583      |
+| 5.2624        | 2.0   | 38   | 5.1901          | 0.2247            | 0.5133         | 0.2910     | 0.8525          | 0.2352       | 0.1174   | 0.225           | 0.4565       | 0.3014   | 0.6426             | 0.4565          | 0.1720      |
+| 4.6987        | 3.0   | 57   | 4.9982          | 0.2978            | 0.6055         | 0.3677     | 0.8057          | 0.2903       | 0.1710   | 0.2788          | 0.5181       | 0.3625   | 0.5895             | 0.5181          | 0.2450      |
+| 4.55          | 4.0   | 76   | 4.7729          | 0.2885            | 0.6683         | 0.3752     | 0.7661          | 0.3656       | 0.1967   | 0.2783          | 0.6232       | 0.3848   | 0.5364             | 0.6232          | 0.2905      |
+| 4.2177        | 5.0   | 95   | 4.5872          | 0.2936            | 0.7137         | 0.3912     | 0.7287          | 0.3965       | 0.2139   | 0.2907          | 0.6594       | 0.4035   | 0.4982             | 0.6594          | 0.3199      |
+| 4.032         | 6.0   | 114  | 4.4578          | 0.3081            | 0.7260         | 0.4059     | 0.7040          | 0.4315       | 0.2385   | 0.2881          | 0.6920       | 0.4068   | 0.4759             | 0.6920          | 0.3423      |
+| 4.0007        | 7.0   | 133  | 4.3653          | 0.3220            | 0.7352         | 0.4198     | 0.6836          | 0.4669       | 0.2688   | 0.2964          | 0.7174       | 0.4195   | 0.4618             | 0.7174          | 0.3671      |
+| 3.8824        | 8.0   | 152  | 4.3266          | 0.3438            | 0.7605         | 0.4395     | 0.6859          | 0.4861       | 0.2784   | 0.3042          | 0.7319       | 0.4298   | 0.4668             | 0.7319          | 0.3779      |
+| 3.819         | 9.0   | 171  | 4.3024          | 0.3296            | 0.7444         | 0.4272     | 0.6865          | 0.4734       | 0.2753   | 0.3015          | 0.7210       | 0.4252   | 0.4659             | 0.7210          | 0.3735      |
+| 4.3455        | 10.0  | 190  | 4.2885          | 0.3350            | 0.7536         | 0.4333     | 0.6879          | 0.4863       | 0.2811   | 0.3050          | 0.7283       | 0.4299   | 0.4670             | 0.7283          | 0.3780      |
+
+
+### Framework versions
+
+- Transformers 4.46.0
+- Pytorch 2.3.1
+- Datasets 2.21.0
+- Tokenizers 0.20.1

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