Text Classification
Transformers
TensorFlow
distilbert
generated_from_keras_callback
text-embeddings-inference
Instructions to use Gasim2024/emotion-analysis with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Gasim2024/emotion-analysis with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Gasim2024/emotion-analysis")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Gasim2024/emotion-analysis") model = AutoModelForSequenceClassification.from_pretrained("Gasim2024/emotion-analysis", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Gasim2024/emotion-analysis
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: 0.1900
- Validation Loss: 0.2545
- Train Accuracy: 0.9094
- 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', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, '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 |
|---|---|---|---|
| 0.3725 | 0.2694 | 0.8859 | 0 |
| 0.2450 | 0.2568 | 0.8990 | 1 |
| 0.1900 | 0.2545 | 0.9094 | 2 |
Framework versions
- Transformers 4.50.3
- TensorFlow 2.18.0
- Datasets 3.5.0
- Tokenizers 0.21.1
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Model tree for Gasim2024/emotion-analysis
Base model
distilbert/distilbert-base-uncased