---
license: mit
tags:
    - generated_from_trainer
datasets:
    - go_emotions
metrics:
    - f1
model-index:
    - name: roberta-large-go-emotions-3
      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: 0.5204
          - 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: 0.5208
---

<!-- 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-large-go-emotions-2

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: 0.4363
- Precision: 0.4955
- Recall: 0.5655
- F1: 0.5204

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## 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: 6

### Training results

| Training Loss | Epoch | Validation Loss | Accuracy | Precision | Recall | F1     |
| ------------- | ----- | --------------- | -------- | --------- | ------ | ------ |
| No log        | 1.0   | 0.0889          | 0.4043   | 0.4807    | 0.4568 | 0.4446 |
| 0.1062        | 2.0   | 0.0828          | 0.4113   | 0.4608    | 0.5363 | 0.4868 |
| 0.1062        | 3.0   | 0.0813          | 0.4201   | 0.5198    | 0.5612 | 0.5227 |
| No log        | 4.0   | 0.0862          | 0.4292   | 0.5012    | 0.5558 | 0.5208 |
| 0.0597        | 5.0   | 0.0924          | 0.4329   | 0.5164    | 0.5362 | 0.5151 |
| 0.0597        | 6.0   | 0.0956          | 0.4445   | 0.5241    | 0.5328 | 0.5161 |

### Framework versions

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