---
license: mit
base_model: microsoft/mdeberta-v3-base
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: scenario_4
  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. -->

# scenario_4

This model is a fine-tuned version of [microsoft/mdeberta-v3-base](https://huggingface.co./microsoft/mdeberta-v3-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1764
- Accuracy: 0.9704
- F1: 0.9704
- Precision: 0.9710
- Recall: 0.9704
- Accuracy Label Test: 0.9879
- Accuracy Label Train: 0.9536

## 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
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 3

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy | F1     | Precision | Recall | Accuracy Label Test | Accuracy Label Train |
|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|:-------------------:|:--------------------:|
| 0.5515        | 0.1579 | 100  | 0.5194          | 0.7579   | 0.7415 | 0.8352    | 0.7579 | 0.5070              | 0.9992               |
| 0.2205        | 0.3157 | 200  | 0.2537          | 0.9300   | 0.9298 | 0.9361    | 0.9300 | 0.9883              | 0.8739               |
| 0.1106        | 0.4736 | 300  | 0.3450          | 0.9129   | 0.9124 | 0.9248    | 0.9129 | 0.9960              | 0.8329               |
| 0.0384        | 0.6314 | 400  | 0.1408          | 0.9683   | 0.9683 | 0.9687    | 0.9683 | 0.9835              | 0.9536               |
| 0.0631        | 0.7893 | 500  | 0.1517          | 0.9631   | 0.9631 | 0.9645    | 0.9631 | 0.9895              | 0.9377               |
| 0.0276        | 0.9471 | 600  | 0.3649          | 0.9387   | 0.9386 | 0.9444    | 0.9387 | 0.9948              | 0.8847               |
| 0.0245        | 1.1050 | 700  | 0.1339          | 0.9702   | 0.9702 | 0.9702    | 0.9702 | 0.9727              | 0.9679               |
| 0.0519        | 1.2628 | 800  | 0.4945          | 0.9186   | 0.9182 | 0.9299    | 0.9186 | 0.9992              | 0.8410               |
| 0.02          | 1.4207 | 900  | 0.2637          | 0.9549   | 0.9548 | 0.9580    | 0.9549 | 0.9960              | 0.9153               |
| 0.0325        | 1.5785 | 1000 | 0.1165          | 0.9708   | 0.9708 | 0.9712    | 0.9708 | 0.9851              | 0.9571               |
| 0.016         | 1.7364 | 1100 | 0.1007          | 0.9692   | 0.9692 | 0.9697    | 0.9692 | 0.9530              | 0.9849               |
| 0.0068        | 1.8942 | 1200 | 0.1679          | 0.9690   | 0.9690 | 0.9697    | 0.9690 | 0.9871              | 0.9516               |
| 0.0042        | 2.0521 | 1300 | 0.1182          | 0.9734   | 0.9734 | 0.9734    | 0.9734 | 0.9723              | 0.9745               |
| 0.0005        | 2.2099 | 1400 | 0.1432          | 0.9730   | 0.9730 | 0.9731    | 0.9730 | 0.9799              | 0.9663               |
| 0.0182        | 2.3678 | 1500 | 0.1460          | 0.9718   | 0.9718 | 0.9723    | 0.9718 | 0.9871              | 0.9571               |
| 0.0004        | 2.5257 | 1600 | 0.1383          | 0.9732   | 0.9732 | 0.9734    | 0.9732 | 0.9843              | 0.9625               |
| 0.0003        | 2.6835 | 1700 | 0.1381          | 0.9744   | 0.9744 | 0.9745    | 0.9744 | 0.9831              | 0.9660               |
| 0.0002        | 2.8414 | 1800 | 0.1599          | 0.9724   | 0.9724 | 0.9728    | 0.9724 | 0.9863              | 0.9590               |


### Framework versions

- Transformers 4.44.0
- Pytorch 2.3.1
- Datasets 2.20.0
- Tokenizers 0.19.1