metadata
license: apache-2.0
library_name: peft
tags:
- parquet
- text-classification
datasets:
- tweet_eval
metrics:
- accuracy
base_model: Giyaseddin/distilroberta-base-finetuned-short-answer-assessment
model-index:
- name: >-
Giyaseddin_distilroberta-base-finetuned-short-answer-assessment-finetuned-lora-tweet_eval_irony
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: tweet_eval
type: tweet_eval
config: irony
split: validation
args: irony
metrics:
- type: accuracy
value: 0.7015706806282722
name: accuracy
Giyaseddin_distilroberta-base-finetuned-short-answer-assessment-finetuned-lora-tweet_eval_irony
This model is a fine-tuned version of Giyaseddin/distilroberta-base-finetuned-short-answer-assessment on the tweet_eval dataset. It achieves the following results on the evaluation set:
- accuracy: 0.7016
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: 0.0005
- 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: linear
- num_epochs: 8
Training results
| accuracy | train_loss | epoch |
|---|---|---|
| 0.4743 | None | 0 |
| 0.6188 | 0.7149 | 0 |
| 0.6262 | 0.6574 | 1 |
| 0.6660 | 0.6179 | 2 |
| 0.6932 | 0.5705 | 3 |
| 0.6859 | 0.5397 | 4 |
| 0.6796 | 0.5244 | 5 |
| 0.6953 | 0.5155 | 6 |
| 0.7016 | 0.4939 | 7 |
Framework versions
- PEFT 0.8.2
- Transformers 4.37.2
- Pytorch 2.2.0
- Datasets 2.16.1
- Tokenizers 0.15.2