End of training

README.md

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+---
+library_name: transformers
+license: mit
+base_model: microsoft/deberta-v3-large
+tags:
+- generated_from_trainer
+model-index:
+- name: deberta-large-semeval25_EN08_fold5
+  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. -->
+
+# deberta-large-semeval25_EN08_fold5
+
+This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 7.7182
+- Precision Samples: 0.1208
+- Recall Samples: 0.8208
+- F1 Samples: 0.2037
+- Precision Macro: 0.3884
+- Recall Macro: 0.6861
+- F1 Macro: 0.2590
+- Precision Micro: 0.1199
+- Recall Micro: 0.7958
+- F1 Micro: 0.2083
+- Precision Weighted: 0.2373
+- Recall Weighted: 0.7958
+- F1 Weighted: 0.2493
+
+## 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: 8
+- eval_batch_size: 8
+- 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 |
+|:-------------:|:-----:|:----:|:---------------:|:-----------------:|:--------------:|:----------:|:---------------:|:------------:|:--------:|:---------------:|:------------:|:--------:|:------------------:|:---------------:|:-----------:|
+| 8.4241        | 1.0   | 73   | 9.1963          | 0.1263            | 0.4598         | 0.1863     | 0.8558          | 0.3082       | 0.2345   | 0.1268          | 0.3514       | 0.1863   | 0.5821             | 0.3514          | 0.1117      |
+| 9.9703        | 2.0   | 146  | 8.5160          | 0.1313            | 0.6118         | 0.1989     | 0.7084          | 0.4108       | 0.2495   | 0.1110          | 0.5495       | 0.1848   | 0.4273             | 0.5495          | 0.1497      |
+| 8.6571        | 3.0   | 219  | 8.3760          | 0.1104            | 0.6956         | 0.1813     | 0.5869          | 0.4853       | 0.2363   | 0.1057          | 0.6366       | 0.1814   | 0.3062             | 0.6366          | 0.1831      |
+| 9.387         | 4.0   | 292  | 8.1585          | 0.1134            | 0.7748         | 0.1885     | 0.5228          | 0.6063       | 0.2487   | 0.1050          | 0.7447       | 0.1840   | 0.2682             | 0.7447          | 0.2017      |
+| 8.4583        | 5.0   | 365  | 8.1996          | 0.1173            | 0.7660         | 0.1960     | 0.4457          | 0.6482       | 0.2512   | 0.1156          | 0.7417       | 0.2001   | 0.2496             | 0.7417          | 0.2253      |
+| 6.3786        | 6.0   | 438  | 7.6840          | 0.1057            | 0.8007         | 0.1802     | 0.4090          | 0.6701       | 0.2410   | 0.1031          | 0.7838       | 0.1822   | 0.2405             | 0.7838          | 0.2289      |
+| 8.2131        | 7.0   | 511  | 7.8402          | 0.1154            | 0.8003         | 0.1953     | 0.3992          | 0.6695       | 0.2514   | 0.1125          | 0.7688       | 0.1963   | 0.2317             | 0.7688          | 0.2324      |
+| 6.8285        | 8.0   | 584  | 7.7532          | 0.1177            | 0.8106         | 0.1991     | 0.3970          | 0.6775       | 0.2552   | 0.1173          | 0.7808       | 0.2040   | 0.2350             | 0.7808          | 0.2416      |
+| 5.5413        | 9.0   | 657  | 7.7258          | 0.1201            | 0.8140         | 0.2027     | 0.3872          | 0.6811       | 0.2571   | 0.1187          | 0.7838       | 0.2062   | 0.2364             | 0.7838          | 0.2474      |
+| 5.9931        | 10.0  | 730  | 7.7182          | 0.1208            | 0.8208         | 0.2037     | 0.3884          | 0.6861       | 0.2590   | 0.1199          | 0.7958       | 0.2083   | 0.2373             | 0.7958          | 0.2493      |
+
+
+### Framework versions
+
+- Transformers 4.46.0
+- Pytorch 2.3.1
+- Datasets 2.21.0
+- Tokenizers 0.20.1

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