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--- |
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license: mit |
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base_model: roberta-base |
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tags: |
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- generated_from_keras_callback |
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model-index: |
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- name: roberta-base-finetuned-sst2 |
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results: [] |
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datasets: |
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- sst2 |
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- glue |
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metrics: |
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- accuracy |
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pipeline_tag: text-classification |
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language: |
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- en |
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widget: |
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- text: "I love video games so much" |
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example_title: "Positive Example" |
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- text: "I don't really like this type of food" |
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example_title: "Negative Example" |
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library_name: transformers |
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--- |
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<!-- This model card has been generated automatically according to the information Keras had access to. You should |
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probably proofread and complete it, then remove this comment. --> |
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# roberta-base-finetuned-sst2 |
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This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the glue [sst2](https://huggingface.co/datasets/sst2) dataset for sentiment classification. |
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It achieves the following results on the evaluation set: |
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- Train Loss: 0.0760 |
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- Train Accuracy: 0.9736 |
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- Validation Loss: 0.2081 |
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- Validation Accuracy: 0.9346 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## How to use |
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You can use this model directly with a pipeline for text classification: |
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```python |
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>>> from transformers import pipeline |
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>>> roberta_sentiment = pipeline("text-classification", model="rasyosef/roberta-base-finetuned-sst2") |
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>>> roberta_sentiment(["This movie was awesome.", "The movie was boring."]) |
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[{'label': 'positive', 'score': 0.9995689988136292}, |
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{'label': 'negative', 'score': 0.9987605810165405}] |
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``` |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- 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} |
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- training_precision: float32 |
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### Training results |
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### Framework versions |
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- Transformers 4.35.2 |
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- TensorFlow 2.15.0 |
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- Datasets 2.16.0 |
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- Tokenizers 0.15.0 |
