Text Classification
Transformers
PyTorch
distilbert
Generated from Trainer
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("IceKingBing/distilbert-imdb")
model = AutoModelForSequenceClassification.from_pretrained("IceKingBing/distilbert-imdb")Quick Links
distilbert-imdb
This model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.
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: 64
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 391 | 0.1847 | 0.9291 |
Framework versions
- Transformers 4.15.0
- Pytorch 1.12.0+cu102
- Datasets 1.17.0
- Tokenizers 0.10.3
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="IceKingBing/distilbert-imdb")