transformers pytorch roberta text-classification generated_from_trainer dataset:emotion base_model:FacebookAI/roberta-base base_model:finetune:FacebookAI/roberta-base license:mit model-index autotrain_compatible endpoints_compatible region:us
---
base_model: roberta-base
datasets:
- emotion
license: mit
metrics:
- accuracy
- precision
- recall
- f1
tags:
- generated_from_trainer
model-index:
- name: RoBERTa-base-finetuned-emotion
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: emotion
      type: emotion
      config: split
      split: test
      args: split
    metrics:
    - type: accuracy
      value: 0.933
      name: Accuracy
    - type: precision
      value: 0.8945201216002613
      name: Precision
    - type: recall
      value: 0.9001524297208578
      name: Recall
    - type: f1
      value: 0.8967563712384394
      name: F1
---

<!-- 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-base-finetuned-emotion

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.
It achieves the following results on the evaluation set:
- Loss: 0.1629
- Accuracy: 0.933
- Precision: 0.8945
- Recall: 0.9002
- F1: 0.8968

## Model description

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: 
'sadness', 'joy', 'love', 'anger', 'fear', 'surprise'. The Trainer API was used to train the model.

## Intended uses & limitations



## Training and evaluation data

 🤗 ``load_dataset`` package was used to load the data from the hub. 

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.5693        | 1.0   | 500  | 0.2305          | 0.9215   | 0.8814    | 0.8854 | 0.8818 |
| 0.1946        | 2.0   | 1000 | 0.1923          | 0.9235   | 0.8698    | 0.9268 | 0.8899 |
| 0.1297        | 3.0   | 1500 | 0.1514          | 0.933    | 0.9060    | 0.8879 | 0.8913 |
| 0.1041        | 4.0   | 2000 | 0.1545          | 0.9265   | 0.9165    | 0.8567 | 0.8789 |
| 0.0826        | 5.0   | 2500 | 0.1629          | 0.933    | 0.8945    | 0.9002 | 0.8968 |


### Framework versions

- Transformers 4.33.0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3
