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tasinhoque
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roberta-large-go-emotions

Text Classification
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roberta-large-go-emotions
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---
license: mit
tags:
    - generated_from_trainer
datasets:
    - go_emotions
metrics:
    - f1
model-index:
    - name: roberta-large-goemotions
      results:
          - task:
                name: Text Classification
                type: text-classification
            dataset:
                name: go_emotions
                type: multilabel_classification
                config: simplified
                split: test
                args: simplified
            metrics:
                - name: F1
                  type: f1
                  value: 0.5102
          - task:
                name: Text Classification
                type: text-classification
            dataset:
                name: go_emotions
                type: multilabel_classification
                config: simplified
                split: validation
                args: simplified
            metrics:
                - name: F1
                  type: f1
                  value: 0.5227
---

<!-- 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. -->

# Text Classification GoEmotions

This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the [go_emotions](https://huggingface.co/datasets/go_emotions) dataset.
It achieves the following results on the test set (with a threshold of 0.15):
- Accuracy: 0.4175
- Precision: 0.4934
- Recall: 0.5621
- F1: 0.5102

## Code

Code for training this model can be found [here](https://github.com/tasinhoque/go-emotions-text-classification).

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:

- learning_rate: 5e-05
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3

### Training results

| Training Loss | Epoch | Validation Loss | Accuracy | Precision |  Recall  |    F1    |
| :-----------: | :---: | :-------------: | :------: | :-------: | :------: | :------: |
|    No log     |  1.0  |    0.088978     | 0.404349 | 0.480763  | 0.456827 | 0.444685 |
|    0.10620    |  2.0  |    0.082806     | 0.411353 | 0.460896  | 0.536386 | 0.486819 |
|    0.10620    |  3.0  |    0.081338     | 0.420199 | 0.519828  | 0.561297 | 0.522716 |

### Framework versions

- Transformers 4.20.1
- Pytorch 1.12.0
- Datasets 2.1.0
- Tokenizers 0.12.1