Text Classification
Transformers
TensorFlow
distilbert
generated_from_keras_callback
text-embeddings-inference
Instructions to use eduardopds/distilbert-base-uncased-imdb with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use eduardopds/distilbert-base-uncased-imdb with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="eduardopds/distilbert-base-uncased-imdb")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("eduardopds/distilbert-base-uncased-imdb") model = AutoModelForSequenceClassification.from_pretrained("eduardopds/distilbert-base-uncased-imdb") - Notebooks
- Google Colab
- Kaggle
eduardopds/distilbert-base-uncased-imdb
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Train Loss: 0.0638
- Validation Loss: 0.2317
- Epoch: 2
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:
- optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 7810, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
Training results
| Train Loss | Validation Loss | Epoch |
|---|---|---|
| 0.2514 | 0.1886 | 0 |
| 0.1340 | 0.1921 | 1 |
| 0.0638 | 0.2317 | 2 |
Framework versions
- Transformers 4.19.0
- TensorFlow 2.8.0
- Tokenizers 0.12.1
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