---
license: mit
base_model: microsoft/deberta-v3-large
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: training1
  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. -->

# training1

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: 0.1739
- Accuracy: 0.9431
- F1: 0.8115
- Precision: 0.8659
- Recall: 0.7636

## 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: 9.946303722432942e-06
- 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
- lr_scheduler_warmup_steps: 500
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.6954        | 1.0   | 61   | 0.6728          | 0.6127   | 0.1189 | 0.0936    | 0.1629 |
| 0.5462        | 2.0   | 122  | 0.3988          | 0.8683   | 0.4352 | 0.6972    | 0.3163 |
| 0.3711        | 3.0   | 183  | 0.3401          | 0.8765   | 0.4703 | 0.7535    | 0.3419 |
| 0.3269        | 4.0   | 244  | 0.3175          | 0.8883   | 0.4785 | 0.9524    | 0.3195 |
| 0.2899        | 5.0   | 305  | 0.2781          | 0.9042   | 0.5961 | 0.92      | 0.4409 |
| 0.2568        | 6.0   | 366  | 0.2576          | 0.9144   | 0.6745 | 0.865     | 0.5527 |
| 0.2176        | 7.0   | 427  | 0.2305          | 0.9242   | 0.7376 | 0.8287    | 0.6645 |
| 0.1879        | 8.0   | 488  | 0.2014          | 0.9329   | 0.7579 | 0.8991    | 0.6550 |
| 0.1541        | 9.0   | 549  | 0.2002          | 0.9329   | 0.7842 | 0.8095    | 0.7604 |
| 0.1275        | 10.0  | 610  | 0.1739          | 0.9431   | 0.8115 | 0.8659    | 0.7636 |


### Framework versions

- Transformers 4.40.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1