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("Siki-77/my_model")
model = AutoModelForSequenceClassification.from_pretrained("Siki-77/my_model")Quick Links
my_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: 0.2247
- Accuracy: 0.9329
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 |
|---|---|---|---|---|
| 0.2238 | 1.0 | 1563 | 0.2247 | 0.9234 |
| 0.1419 | 2.0 | 3126 | 0.2247 | 0.9329 |
Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cpu
- Datasets 2.14.5
- Tokenizers 0.13.3
- Downloads last month
- 5
Model tree for Siki-77/my_model
Base model
distilbert/distilbert-base-uncasedDataset used to train Siki-77/my_model
Evaluation results
- Accuracy on imdbtest set self-reported0.933
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Siki-77/my_model")