---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
base_model: microsoft/deberta-v3-small
model-index:
- name: deberta-v3-small-isarcasm
  results:
  - task:
      type: text-classification
    dataset:
      name: iSarcasm
      type: isarcasm
      split: test
    metrics:
    - type: f1
      value: 0.44808743169398907
      name: f1
    - type: accuracy
      value: 0.7722660653889515
      name: accuracy
    - type: precision
      value: 0.3923444976076555
      name: precision
    - type: recall
      value: 0.5222929936305732
      name: recall
---

<!-- 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-isarcasm

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: 0.6010
- Accuracy: 0.7723
- F1: 0.4481
- Precision: 0.3923
- Recall: 0.5223

## 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: 8
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| No log        | 1.0   | 429  | 0.6567          | 0.8286   | 0.0    | 0.0       | 0.0    |
| 0.6792        | 2.0   | 858  | 0.5566          | 0.8286   | 0.625  | 0.5       | 0.8333 |
| 0.5916        | 3.0   | 1287 | 1.6155          | 0.7714   | 0.0    | 0.0       | 0.0    |
| 0.4278        | 4.0   | 1716 | 1.9964          | 0.7429   | 0.1818 | 0.2       | 0.1667 |
| 0.2417        | 5.0   | 2145 | 2.1995          | 0.7714   | 0.2    | 0.25      | 0.1667 |


### Framework versions

- Transformers 4.32.0
- Pytorch 1.13.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3