|
--- |
|
license: apache-2.0 |
|
library_name: peft |
|
tags: |
|
- generated_from_trainer |
|
metrics: |
|
- accuracy |
|
base_model: distilbert-base-uncased |
|
model-index: |
|
- name: distilbert-base-uncased-lora-imdb-sentiment |
|
results: [] |
|
--- |
|
|
|
<!-- 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. --> |
|
|
|
# distilbert-base-uncased-lora-imdb-sentiment |
|
|
|
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.6932 |
|
- Accuracy: 0.4968 |
|
|
|
## 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: 0.001 |
|
- train_batch_size: 4 |
|
- eval_batch_size: 4 |
|
- seed: 42 |
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
- lr_scheduler_type: linear |
|
- num_epochs: 10 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
|
|:-------------:|:-----:|:-----:|:---------------:|:--------:| |
|
| 0.6937 | 1.0 | 7500 | 0.6977 | 0.5032 | |
|
| 0.6952 | 2.0 | 15000 | 0.6931 | 0.5032 | |
|
| 0.6933 | 3.0 | 22500 | 0.6933 | 0.4968 | |
|
| 0.6933 | 4.0 | 30000 | 0.6931 | 0.5032 | |
|
| 0.6932 | 5.0 | 37500 | 0.6931 | 0.5032 | |
|
| 0.6933 | 6.0 | 45000 | 0.6932 | 0.4968 | |
|
| 0.6931 | 7.0 | 52500 | 0.6932 | 0.4968 | |
|
| 0.6933 | 8.0 | 60000 | 0.6931 | 0.5032 | |
|
| 0.6931 | 9.0 | 67500 | 0.6932 | 0.4968 | |
|
| 0.6932 | 10.0 | 75000 | 0.6932 | 0.4968 | |
|
|
|
|
|
### Framework versions |
|
|
|
- PEFT 0.8.2 |
|
- Transformers 4.37.2 |
|
- Pytorch 2.1.0+cu121 |
|
- Datasets 2.17.1 |
|
- Tokenizers 0.15.1 |
