transformers pytorch roberta text-classification generated_from_trainer dataset:emotion model-index autotrain_compatible endpoints_compatible region:us
---
datasets:
- emotion
metrics:
- accuracy
tags:
- generated_from_trainer
model-index:
- name: bnb-sentiment-model-saagie
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: emotion
      type: emotion
      args: split
    metrics:
    - type: accuracy
      value: 0.9444444444444444
      name: Accuracy
---

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# bnb-sentiment-model-saagie

This model is a fine-tuned version of [j-hartmann/emotion-english-distilroberta-base](https://huggingface.co/j-hartmann/emotion-english-distilroberta-base) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3581
- Accuracy: 0.9444

## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.3724        | 1.0   | 1875  | 0.1799          | 0.9367   |
| 0.2118        | 2.0   | 3750  | 0.1918          | 0.9456   |
| 0.1792        | 3.0   | 5625  | 0.1791          | 0.95     |
| 0.1489        | 4.0   | 7500  | 0.1479          | 0.9489   |
| 0.1168        | 5.0   | 9375  | 0.2561          | 0.9444   |
| 0.081         | 6.0   | 11250 | 0.2863          | 0.9411   |
| 0.0521        | 7.0   | 13125 | 0.3168          | 0.9467   |
| 0.0345        | 8.0   | 15000 | 0.3581          | 0.9444   |


### Framework versions

- Transformers 4.18.0
- Pytorch 1.8.1
- Datasets 2.12.0
- Tokenizers 0.12.1
