metadata
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: results
results: []
results
This model is a fine-tuned version of distilbert-base-uncased on the sentiment140 (https://huggingface.co/datasets/sentiment140) dataset.
Due to time and computational limits, I used only 10% of the dataset with a data augmentation. Despite minimal training, model's accuracy improved by 59.5% in comparison with non-fine tuned DistilBert LLM!
It achieves the following results on the evaluation set:
- Loss: 0.4732
- Accuracy: 0.7753
Classification Scores:
Fine Tuned DistilBert LLM with LoRa: Accuracy = 0.78, Precision = 0.77, Recall = 0.78, F1 Score = 0.78
Non-Fine Tuned DistilBert LLM: Accuracy = 0.49, Precision = 0.45, Recall = 0.14, F1 Score = 0.22
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: 5e-05
- train_batch_size: 32
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.5092 | 1.0 | 5688 | 0.4979 | 0.7591 |
| 0.4928 | 2.0 | 11376 | 0.4876 | 0.7662 |
| 0.4915 | 3.0 | 17064 | 0.4821 | 0.7711 |
| 0.4787 | 4.0 | 22752 | 0.4779 | 0.7731 |
| 0.4757 | 5.0 | 28440 | 0.4767 | 0.7746 |
| 0.473 | 6.0 | 34128 | 0.4743 | 0.775 |
| 0.4649 | 7.0 | 39816 | 0.4741 | 0.7751 |
| 0.4709 | 8.0 | 45504 | 0.4732 | 0.7753 |
Framework versions
- Transformers 4.39.3
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2