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