Text Classification
Transformers
TensorBoard
Safetensors
distilbert
Generated from Trainer
sentiment-analysis
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("DerivedFunction01/distilbert-imdb")
model = AutoModelForSequenceClassification.from_pretrained("DerivedFunction01/distilbert-imdb")Quick Links
distilbert-imdb
This model is a fine-tuned version of distilbert/distilbert-base-cased on dataset. It achieves the following results on the evaluation set:
- Loss: 0.2111
- Accuracy: 0.9285
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: 24
- eval_batch_size: 24
- 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: 2
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.2040 | 0.4997 | 833 | 0.2493 | 0.8981 |
| 0.1805 | 0.9994 | 1666 | 0.2134 | 0.915 |
| 0.1683 | 1.4991 | 2499 | 0.2222 | 0.9262 |
| 0.1644 | 1.9988 | 3332 | 0.2111 | 0.9285 |
Framework versions
- Transformers 5.0.0
- Pytorch 2.10.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.2
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Model tree for DerivedFunction01/distilbert-imdb
Base model
distilbert/distilbert-base-cased
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="DerivedFunction01/distilbert-imdb")