|
--- |
|
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 |
|
|
