Text Classification
Transformers
TensorBoard
Safetensors
distilbert
Generated from Trainer
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("davidop97/imdbreviews_classification_distilbert_v02")
model = AutoModelForSequenceClassification.from_pretrained("davidop97/imdbreviews_classification_distilbert_v02")Quick Links
imdbreviews_classification_distilbert_v02
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: 0.2807
- Accuracy: 0.933
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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.2092 | 1.0 | 2000 | 0.1913 | 0.9266 |
| 0.1406 | 2.0 | 4000 | 0.2277 | 0.9331 |
| 0.0908 | 3.0 | 6000 | 0.2807 | 0.933 |
Framework versions
- Transformers 4.51.1
- Pytorch 2.5.1+cu124
- Datasets 3.5.0
- Tokenizers 0.21.0
- Downloads last month
- 2
Model tree for davidop97/imdbreviews_classification_distilbert_v02
Base model
distilbert/distilbert-base-uncased
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="davidop97/imdbreviews_classification_distilbert_v02")