Text Classification
Transformers
TensorFlow
distilbert
generated_from_keras_callback
text-embeddings-inference
Instructions to use Jaya4/emotion-analysis-with-distilbert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jaya4/emotion-analysis-with-distilbert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jaya4/emotion-analysis-with-distilbert")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jaya4/emotion-analysis-with-distilbert") model = AutoModelForSequenceClassification.from_pretrained("Jaya4/emotion-analysis-with-distilbert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Jaya4/emotion-analysis-with-distilbert
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: 0.1352
- Validation Loss: 0.1599
- Train Accuracy: 0.9335
- Epoch: 1
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', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': np.float32(5e-05), 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32
Training results
| Train Loss | Validation Loss | Train Accuracy | Epoch |
|---|---|---|---|
| 0.3734 | 0.1721 | 0.927 | 0 |
| 0.1352 | 0.1599 | 0.9335 | 1 |
Framework versions
- Transformers 4.57.3
- TensorFlow 2.19.0
- Datasets 4.0.0
- Tokenizers 0.22.1
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Model tree for Jaya4/emotion-analysis-with-distilbert
Base model
distilbert/distilbert-base-uncased