metadata
license: mit
library_name: peft
tags:
- generated_from_trainer
base_model: openai-community/gpt2
datasets:
- emotion
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: emotion-gpt2-lora
results: []
emotion-gpt2-lora
This model is a fine-tuned version of openai-community/gpt2 on the emotion dataset. It achieves the following results on the evaluation set:
- Loss: 0.1521
- Accuracy: 0.933
- F1: 0.9334
- Precision: 0.9347
- Recall: 0.933
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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 250 | 0.3191 | 0.8895 | 0.8902 | 0.8933 | 0.8895 |
| 0.6939 | 2.0 | 500 | 0.1939 | 0.935 | 0.9349 | 0.9352 | 0.935 |
| 0.6939 | 3.0 | 750 | 0.1689 | 0.931 | 0.9315 | 0.9329 | 0.931 |
| 0.1897 | 4.0 | 1000 | 0.1521 | 0.933 | 0.9334 | 0.9347 | 0.933 |
Framework versions
- PEFT 0.10.0
- Transformers 4.40.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1