transformers pytorch tensorboard roberta text-classification generated_from_trainer dataset:go_emotions license:mit model-index autotrain_compatible endpoints_compatible region:us
---
datasets:
- go_emotions
license: mit
metrics:
- f1
tags:
- generated_from_trainer
model-index:
- name: emotion_classification
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: go_emotions
      type: go_emotions
      config: simplified
      split: validation
      args: simplified
    metrics:
    - type: f1
      value: 0.38517334250011687
      name: F1
---

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# emotion_classification

This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the go_emotions dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6119
- F1: 0.3852

## 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: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- 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 | Step | Validation Loss | F1     |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log        | 1.0   | 313  | 1.8826          | 0.1762 |
| 2.1614        | 2.0   | 626  | 1.6738          | 0.3442 |
| 2.1614        | 3.0   | 939  | 1.6119          | 0.3852 |


### Framework versions

- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
