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---
license: mit
base_model: microsoft/deberta-v3-small
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: my_awesome_model
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. -->
# my_awesome_model
This model is a fine-tuned version of [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0808
- Accuracy: 0.8289
- F1: 0.8595
- Precision: 0.8864
- Recall: 0.8342
## 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: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| No log | 1.0 | 47 | 0.1056 | 0.7059 | 0.7788 | 0.8684 | 0.7059 |
| No log | 2.0 | 94 | 0.0961 | 0.7219 | 0.7895 | 0.8710 | 0.7219 |
| No log | 3.0 | 141 | 0.1042 | 0.7594 | 0.8045 | 0.8554 | 0.7594 |
| No log | 4.0 | 188 | 0.0899 | 0.8021 | 0.8427 | 0.8876 | 0.8021 |
| No log | 5.0 | 235 | 0.0911 | 0.8182 | 0.8540 | 0.8807 | 0.8289 |
| No log | 6.0 | 282 | 0.0808 | 0.8289 | 0.8595 | 0.8864 | 0.8342 |
| No log | 7.0 | 329 | 0.0885 | 0.8503 | 0.8689 | 0.8883 | 0.8503 |
| No log | 8.0 | 376 | 0.0873 | 0.8396 | 0.8634 | 0.8827 | 0.8449 |
| No log | 9.0 | 423 | 0.0926 | 0.8342 | 0.8579 | 0.8771 | 0.8396 |
| No log | 10.0 | 470 | 0.0904 | 0.8342 | 0.8603 | 0.8820 | 0.8396 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1
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