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--- |
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license: apache-2.0 |
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base_model: distilbert-base-uncased |
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tags: |
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- generated_from_keras_callback |
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model-index: |
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- name: digo-prayudha/Indonesian_sentiment |
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results: [] |
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language: |
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- id |
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pipeline_tag: text-classification |
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datasets: |
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- sepidmnorozy/Indonesian_sentiment |
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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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# digo-prayudha/Indonesian_sentiment |
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This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on [sepidmnorozy/Indonesian_sentiment](https://huggingface.co/datasets/sepidmnorozy/Indonesian_sentiment). |
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It achieves the following results on the evaluation set: |
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- Train Loss: 0.1678 |
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- Validation Loss: 0.2402 |
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- Train Accuracy: 0.9016 |
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- Epoch: 2 |
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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': 2e-05, 'decay_steps': 2475, '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} |
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- training_precision: float32 |
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### Training results |
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| Train Loss | Validation Loss | Train Accuracy | Epoch | |
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|:----------:|:---------------:|:--------------:|:-----:| |
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| 0.4013 | 0.3141 | 0.8667 | 0 | |
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| 0.2526 | 0.2923 | 0.8839 | 1 | |
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| 0.1678 | 0.2402 | 0.9016 | 2 | |
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### How to use this model in Transformers Library |
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```python |
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from transformers import pipeline |
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model = pipeline("text-classification",model="digo-prayudha/Indonesian_sentiment") |
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model("Makanannya Enak sekali!") |
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``` |
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### Framework versions |
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- Transformers 4.35.2 |
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- TensorFlow 2.14.0 |
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- Datasets 2.15.0 |
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- Tokenizers 0.15.0 |
