|
--- |
|
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 |
|
|
