---
license: mit
base_model: microsoft/deberta-v3-small
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
model-index:
- name: deberta-v3-small-nslp-forc-subtask1
  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-small-nslp-forc-subtask1

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: 0.2167
- Accuracy: 0.6649
- Precision: 0.6642
- Recall: 0.6649
- F1-weighted: 0.6595

## 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: 5e-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: 5

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy | Precision | Recall | F1-weighted |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:---------:|:------:|:-----------:|
| 0.3563        | 0.77  | 2000  | 0.3333          | 0.5035   | 0.4651    | 0.5035 | 0.4562      |
| 0.2443        | 1.54  | 4000  | 0.2647          | 0.5708   | 0.5598    | 0.5708 | 0.5484      |
| 0.1736        | 2.31  | 6000  | 0.2359          | 0.6152   | 0.6105    | 0.6152 | 0.5969      |
| 0.1404        | 3.08  | 8000  | 0.2207          | 0.6424   | 0.6391    | 0.6424 | 0.6250      |
| 0.1109        | 3.85  | 10000 | 0.2181          | 0.6581   | 0.6534    | 0.6581 | 0.6490      |
| 0.0817        | 4.62  | 12000 | 0.2167          | 0.6649   | 0.6642    | 0.6649 | 0.6595      |


### Framework versions

- Transformers 4.37.2
- Pytorch 2.1.2
- Datasets 2.17.0
- Tokenizers 0.15.1