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