Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("chjun/my_awesome_model")
model = AutoModelForSequenceClassification.from_pretrained("chjun/my_awesome_model")Quick Links
my_awesome_model
This model is a fine-tuned version of distilbert-base-uncased on the imdb dataset. It achieves the following results on the evaluation set:
- Loss: 0.2372
- Accuracy: 0.9305
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: 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 |
|---|---|---|---|---|
| 0.2346 | 1.0 | 1563 | 0.1895 | 0.9280 |
| 0.1531 | 2.0 | 3126 | 0.2372 | 0.9305 |
Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
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Dataset used to train chjun/my_awesome_model
Spaces using chjun/my_awesome_model 3
Evaluation results
- Accuracy on imdbtest set self-reported0.931
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="chjun/my_awesome_model")