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---
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
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