roberta-base-finetuned-sst2
This model is a fine-tuned version of roberta-base on the glue sst2 dataset for sentiment classification. It achieves the following results on the evaluation set:
- Train Loss: 0.0760
- Train Accuracy: 0.9736
- Validation Loss: 0.2081
- Validation Accuracy: 0.9346
Model description
More information needed
Intended uses & limitations
More information needed
How to use
You can use this model directly with a pipeline for text classification:
>>> from transformers import pipeline
>>> roberta_sentiment = pipeline("text-classification", model="rasyosef/roberta-base-finetuned-sst2")
>>> roberta_sentiment(["This movie was awesome.", "The movie was boring."])
[{'label': 'positive', 'score': 0.9995689988136292},
{'label': 'negative', 'score': 0.9987605810165405}]
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': 5e-05, 'decay_steps': 3159, '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-07, 'amsgrad': False}
- training_precision: float32
Training results
Framework versions
- Transformers 4.35.2
- TensorFlow 2.15.0
- Datasets 2.16.0
- Tokenizers 0.15.0
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