distilbert-base-uncased-finetuned-twitter-sentiment – AI Model by arungopi | AlphaNeural AI

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arungopi/
distilbert-base-uncased-finetuned-twitter-sentiment

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text-classification

generated_from_keras_callback

distilbert/distilbert-base-uncased

autotrain_compatible

endpoints_compatible

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arungopi/distilbert-base-uncased-finetuned-twitter-sentiment

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: 0.0183
Validation Loss: 0.2731
Train Accuracy: 0.9375
Epoch: 7

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': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 27904, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
training_precision: float32

Training results

Train Loss	Validation Loss	Train Accuracy	Epoch
0.8261	0.5284	0.8079	0
0.3382	0.3073	0.8976	1
0.1365	0.2687	0.9176	2
0.0753	0.2875	0.9225	3
0.0487	0.2685	0.9303	4
0.0320	0.2791	0.9322	5
0.0243	0.2819	0.9347	6
0.0183	0.2731	0.9375	7

Framework versions

Transformers 4.53.2
TensorFlow 2.18.0
Datasets 2.14.4
Tokenizers 0.21.2