Text Classification
Transformers
TensorBoard
Safetensors
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use Chima207/distilbert_amazon_goodreads_book_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Chima207/distilbert_amazon_goodreads_book_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Chima207/distilbert_amazon_goodreads_book_classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Chima207/distilbert_amazon_goodreads_book_classification") model = AutoModelForSequenceClassification.from_pretrained("Chima207/distilbert_amazon_goodreads_book_classification") - Notebooks
- Google Colab
- Kaggle
distilbert_amazon_goodreads_book_classification
This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.7274
- Accuracy: 0.5135
- F1 Score: 0.4989
- Precision: 0.5060
- Recall: 0.5135
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: 2e-05
- train_batch_size: 4
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Score | Precision | Recall |
|---|---|---|---|---|---|---|---|
| 0.414 | 1.0000 | 8519 | 1.7274 | 0.5135 | 0.4989 | 0.5060 | 0.5135 |
| 0.237 | 1.9999 | 17038 | 2.0405 | 0.5210 | 0.5118 | 0.5146 | 0.5210 |
Framework versions
- Transformers 4.45.2
- Pytorch 2.5.1
- Datasets 4.1.1
- Tokenizers 0.20.1
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