README.md

---
license: mit
base_model: microsoft/deberta-v3-large
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: deberta-v3-large-262-ver1
  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-large-262-ver1

This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0768
- Precision: 0.9904
- Recall: 0.9904
- F1: 0.9904
- Accuracy: 0.9904

## 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: 12
- eval_batch_size: 12
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0624        | 1.0   | 1287 | 0.0447          | 0.9907    | 0.9907 | 0.9907 | 0.9907   |
| 0.0272        | 2.0   | 2574 | 0.0899          | 0.9865    | 0.9865 | 0.9865 | 0.9865   |
| 0.0136        | 3.0   | 3861 | 0.0605          | 0.9894    | 0.9894 | 0.9894 | 0.9894   |
| 0.0071        | 4.0   | 5148 | 0.0771          | 0.9894    | 0.9894 | 0.9894 | 0.9894   |
| 0.0015        | 5.0   | 6435 | 0.0768          | 0.9904    | 0.9904 | 0.9904 | 0.9904   |


### Framework versions

- Transformers 4.44.0
- Pytorch 2.2.0
- Datasets 2.20.0
- Tokenizers 0.19.1