---
license: mit
base_model: microsoft/deberta-v3-base
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: deberta-v3-base-p-tuning-isarcasm
  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-base-p-tuning-isarcasm

This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6620
- Accuracy: 0.4476
- F1: 0.3603
- Precision: 0.2266
- Recall: 0.8790

## 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: 0.001
- train_batch_size: 32
- eval_batch_size: 32
- 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: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| No log        | 1.0   | 108  | 0.6889          | 0.3143   | 0.2941 | 0.1786    | 0.8333 |
| No log        | 2.0   | 216  | 0.7020          | 0.8286   | 0.0    | 0.0       | 0.0    |
| No log        | 3.0   | 324  | 0.6725          | 0.6286   | 0.2353 | 0.1818    | 0.3333 |
| No log        | 4.0   | 432  | 0.7169          | 0.1714   | 0.2927 | 0.1714    | 1.0    |
| 0.7087        | 5.0   | 540  | 0.6925          | 0.5429   | 0.3846 | 0.25      | 0.8333 |
| 0.7087        | 6.0   | 648  | 0.6991          | 0.1714   | 0.2927 | 0.1714    | 1.0    |
| 0.7087        | 7.0   | 756  | 0.6780          | 0.8286   | 0.0    | 0.0       | 0.0    |
| 0.7087        | 8.0   | 864  | 0.6851          | 0.8286   | 0.0    | 0.0       | 0.0    |
| 0.7087        | 9.0   | 972  | 0.6712          | 0.8286   | 0.0    | 0.0       | 0.0    |
| 0.7055        | 10.0  | 1080 | 0.6767          | 0.3143   | 0.3333 | 0.2       | 1.0    |
| 0.7055        | 11.0  | 1188 | 0.6720          | 0.5714   | 0.4000 | 0.2632    | 0.8333 |
| 0.7055        | 12.0  | 1296 | 0.6710          | 0.3714   | 0.3529 | 0.2143    | 1.0    |
| 0.7055        | 13.0  | 1404 | 0.6676          | 0.4857   | 0.3077 | 0.2       | 0.6667 |
| 0.6916        | 14.0  | 1512 | 0.6735          | 0.3714   | 0.3125 | 0.1923    | 0.8333 |
| 0.6916        | 15.0  | 1620 | 0.6762          | 0.3714   | 0.3529 | 0.2143    | 1.0    |
| 0.6916        | 16.0  | 1728 | 0.6642          | 0.6286   | 0.3158 | 0.2308    | 0.5    |
| 0.6916        | 17.0  | 1836 | 0.6609          | 0.5143   | 0.32   | 0.2105    | 0.6667 |
| 0.6916        | 18.0  | 1944 | 0.6632          | 0.4571   | 0.2963 | 0.1905    | 0.6667 |
| 0.6798        | 19.0  | 2052 | 0.6640          | 0.4      | 0.2759 | 0.1739    | 0.6667 |
| 0.6798        | 20.0  | 2160 | 0.6644          | 0.4      | 0.2759 | 0.1739    | 0.6667 |


### Framework versions

- Transformers 4.32.0
- Pytorch 2.1.1+cu121
- Datasets 2.14.5
- Tokenizers 0.13.3