metadata
library_name: peft
tags:
- parquet
- text-classification
datasets:
- tweet_eval
metrics:
- accuracy
base_model: moshew/bert-mini-sst2-distilled
model-index:
- name: moshew_bert-mini-sst2-distilled-finetuned-lora-tweet_eval_sentiment
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: tweet_eval
type: tweet_eval
config: sentiment
split: validation
args: sentiment
metrics:
- type: accuracy
value: 0.6765
name: accuracy
moshew_bert-mini-sst2-distilled-finetuned-lora-tweet_eval_sentiment
This model is a fine-tuned version of moshew/bert-mini-sst2-distilled on the tweet_eval dataset. It achieves the following results on the evaluation set:
- accuracy: 0.6765
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: 0.0004
- 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: 4
Training results
| accuracy | train_loss | epoch |
|---|---|---|
| 0.2375 | None | 0 |
| 0.6665 | 0.7985 | 0 |
| 0.672 | 0.7376 | 1 |
| 0.675 | 0.7293 | 2 |
| 0.6765 | 0.7231 | 3 |
Framework versions
- PEFT 0.8.2
- Transformers 4.37.2
- Pytorch 2.2.0
- Datasets 2.16.1
- Tokenizers 0.15.2