Text Classification
Transformers
PyTorch
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("genaibook/classifier-chapter4")
model = AutoModelForSequenceClassification.from_pretrained("genaibook/classifier-chapter4")Quick Links
trainer-chapter4
This model is a fine-tuned version of distilbert-base-uncased on the ag_news dataset. It achieves the following results on the evaluation set:
- Loss: 0.2713
- Accuracy: 0.9130
- F1: 0.9130
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: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| No log | 1.0 | 313 | 0.2816 | 0.9046 | 0.9042 |
| 0.2967 | 2.0 | 626 | 0.2713 | 0.9130 | 0.9130 |
Framework versions
- Transformers 4.34.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.14.0
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Model tree for genaibook/classifier-chapter4
Base model
distilbert/distilbert-base-uncasedDataset used to train genaibook/classifier-chapter4
Evaluation results
- Accuracy on ag_newstest set self-reported0.913
- F1 on ag_newstest set self-reported0.913
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="genaibook/classifier-chapter4")