2025-24679-text-distilbert-predictor
This model is a fine-tuned version of
distilbert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0451
- Accuracy: 1.0
- F1: 1.0
- Precision: 1.0
- Recall: 1.0
Model description
Purpose:
This model to be used for in-class assignments and activity associated with Course 24679 at CMU.
Preprocessing/Augmentation:
The preprocessing of this data includes splitting the dataset into train and
test, and using autoML to predict whether the book from the dataset will be
reccomended The peredictor weas fitting using a 20 minute time limit, in
addition to a best_quality present and auto_stacking to improve accuracy
over the constrained timeframe.
Intended uses & limitations
Intented use/limits:
The intended use of this dataset is exclusively for classroom and assignment
use. Please request permission if you wish to use it elsewhere
Ethical notes:
The AI used in this sample is a fairly benign use case handling basic
text manipulation. However, please review the environmental impacts of
large-scale usage in exchange for implementing the necessary good such
technology brings.
AI Usage disclosure:
Original code to assist with data augmentation was developed with use of
Google Gemini in combination with course material from 24679 at CMU
Training and evaluation data
limitations:
Dataset trained on is a simple text dataset for books, and has been
trained to classify the reccomended column which is a simple binary. This
training model is quite minimal as a result
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|---|
| 0.0527 | 1.0 | 80 | 0.0426 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0088 | 2.0 | 160 | 0.0070 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.006 | 3.0 | 240 | 0.0041 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0042 | 4.0 | 320 | 0.0032 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0042 | 5.0 | 400 | 0.0029 | 1.0 | 1.0 | 1.0 | 1.0 |
Framework versions
- Transformers 4.56.1
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.0
