Model save

README.md

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+---
+license: mit
+base_model: microsoft/deberta-v3-base
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+model-index:
+- name: deberta-v3-base_finetuned_nostalgia
+  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-v3-base_finetuned_nostalgia
+
+This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.3772
+- Accuracy: 0.9379
+- F1 Macro: 0.9288
+- Accuracy Balanced: 0.9264
+- F1 Micro: 0.9379
+- Precision Macro: 0.9313
+- Recall Macro: 0.9264
+- Precision Micro: 0.9379
+- Recall Micro: 0.9379
+
+## 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: 64
+- seed: 1984
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_ratio: 0.06
+- num_epochs: 10
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Macro | Accuracy Balanced | F1 Micro | Precision Macro | Recall Macro | Precision Micro | Recall Micro |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:-----------------:|:--------:|:---------------:|:------------:|:---------------:|:------------:|
+| No log        | 1.0   | 73   | 0.3903          | 0.8759   | 0.8470   | 0.8251            | 0.8759   | 0.8890          | 0.8251       | 0.8759          | 0.8759       |
+| No log        | 2.0   | 146  | 0.2130          | 0.9103   | 0.8933   | 0.8783            | 0.9103   | 0.9149          | 0.8783       | 0.9103          | 0.9103       |
+| No log        | 3.0   | 219  | 0.1253          | 0.9379   | 0.9288   | 0.9264            | 0.9379   | 0.9313          | 0.9264       | 0.9379          | 0.9379       |
+| No log        | 4.0   | 292  | 0.2694          | 0.9310   | 0.9229   | 0.9324            | 0.9310   | 0.9154          | 0.9324       | 0.9310          | 0.9310       |
+| No log        | 5.0   | 365  | 0.1924          | 0.9448   | 0.9370   | 0.9370            | 0.9448   | 0.9370          | 0.9370       | 0.9448          | 0.9448       |
+| No log        | 6.0   | 438  | 0.2648          | 0.9379   | 0.9288   | 0.9264            | 0.9379   | 0.9313          | 0.9264       | 0.9379          | 0.9379       |
+| 0.1908        | 7.0   | 511  | 0.3431          | 0.9379   | 0.9288   | 0.9264            | 0.9379   | 0.9313          | 0.9264       | 0.9379          | 0.9379       |
+| 0.1908        | 8.0   | 584  | 0.3450          | 0.9379   | 0.9288   | 0.9264            | 0.9379   | 0.9313          | 0.9264       | 0.9379          | 0.9379       |
+| 0.1908        | 9.0   | 657  | 0.3538          | 0.9379   | 0.9279   | 0.9209            | 0.9379   | 0.9362          | 0.9209       | 0.9379          | 0.9379       |
+| 0.1908        | 10.0  | 730  | 0.3772          | 0.9379   | 0.9288   | 0.9264            | 0.9379   | 0.9313          | 0.9264       | 0.9379          | 0.9379       |
+
+
+### Framework versions
+
+- Transformers 4.40.0
+- Pytorch 2.2.1+cu121
+- Datasets 2.19.1
+- Tokenizers 0.19.1

model.safetensors

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