| --- |
| license: mit |
| base_model: roberta-base |
| tags: |
| - generated_from_trainer |
| datasets: |
| - emotion |
| metrics: |
| - accuracy |
| - precision |
| - recall |
| - f1 |
| model-index: |
| - name: RoBERTa-base-finetuned-emotion |
| results: |
| - task: |
| name: Text Classification |
| type: text-classification |
| dataset: |
| name: emotion |
| type: emotion |
| config: split |
| split: test |
| args: split |
| metrics: |
| - name: Accuracy |
| type: accuracy |
| value: 0.933 |
| - name: Precision |
| type: precision |
| value: 0.8945201216002613 |
| - name: Recall |
| type: recall |
| value: 0.9001524297208578 |
| - name: F1 |
| type: f1 |
| value: 0.8967563712384394 |
| --- |
| |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| should probably proofread and complete it, then remove this comment. --> |
|
|
| # RoBERTa-base-finetuned-emotion |
|
|
| This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the [emotion](https://huggingface.co/datasets/dair-ai/emotion) dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.1629 |
| - Accuracy: 0.933 |
| - Precision: 0.8945 |
| - Recall: 0.9002 |
| - F1: 0.8968 |
|
|
| ## Model description |
|
|
| This is a RoBERTa model fine-tuned on the [emotion](https://huggingface.co/datasets/dair-ai/emotion) to determine whether a text is within any of the six categories: |
| 'sadness', 'joy', 'love', 'anger', 'fear', 'surprise'. The Trainer API was used to train the model. |
|
|
| ## Intended uses & limitations |
|
|
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|
|
| ## Training and evaluation data |
|
|
| 🤗 ``load_dataset`` package was used to load the data from the hub. |
|
|
| ## Training procedure |
|
|
| ### Training hyperparameters |
|
|
| The following hyperparameters were used during training: |
| - learning_rate: 2e-05 |
| - train_batch_size: 32 |
| - eval_batch_size: 32 |
| - seed: 42 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: linear |
| - num_epochs: 5 |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| |
| | 0.5693 | 1.0 | 500 | 0.2305 | 0.9215 | 0.8814 | 0.8854 | 0.8818 | |
| | 0.1946 | 2.0 | 1000 | 0.1923 | 0.9235 | 0.8698 | 0.9268 | 0.8899 | |
| | 0.1297 | 3.0 | 1500 | 0.1514 | 0.933 | 0.9060 | 0.8879 | 0.8913 | |
| | 0.1041 | 4.0 | 2000 | 0.1545 | 0.9265 | 0.9165 | 0.8567 | 0.8789 | |
| | 0.0826 | 5.0 | 2500 | 0.1629 | 0.933 | 0.8945 | 0.9002 | 0.8968 | |
|
|
|
|
| ### Framework versions |
|
|
| - Transformers 4.33.0 |
| - Pytorch 2.0.0 |
| - Datasets 2.1.0 |
| - Tokenizers 0.13.3 |
|
|
