metadata

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

RoBERTa-base-finetuned-emotion

This model is a fine-tuned version of roberta-base on the 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 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