metadata
library_name: peft
tags:
- parquet
- text-classification
datasets:
- tweet_eval
metrics:
- accuracy
base_model: jb2k/bert-base-multilingual-cased-language-detection
model-index:
- name: >-
jb2k_bert-base-multilingual-cased-language-detection-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: 0.5905759162303665
name: accuracy
jb2k_bert-base-multilingual-cased-language-detection-finetuned-lora-tweet_eval_irony
This model is a fine-tuned version of jb2k/bert-base-multilingual-cased-language-detection on the tweet_eval dataset. It achieves the following results on the evaluation set:
- accuracy: 0.5906
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 |
|---|---|---|
| 0.4806 | None | 0 |
| 0.5476 | 0.6896 | 0 |
| 0.5654 | 0.6710 | 1 |
| 0.5738 | 0.6549 | 2 |
| 0.5770 | 0.6416 | 3 |
| 0.5801 | 0.6325 | 4 |
| 0.5853 | 0.6288 | 5 |
| 0.5853 | 0.6242 | 6 |
| 0.5906 | 0.6154 | 7 |
Framework versions
- PEFT 0.8.2
- Transformers 4.37.2
- Pytorch 2.2.0
- Datasets 2.16.1
- Tokenizers 0.15.2