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---
tags:
- generated_from_trainer
datasets:
- tweet_eval
metrics:
- accuracy
- f1
model-index:
- name: twitter-roberta-base-mar2022-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: tweet_eval
type: tweet_eval
args: emotion
metrics:
- name: Accuracy
type: accuracy
value: 0.8191414496833216
- name: F1
type: f1
value: 0.8170974933422602
---
<!-- 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. -->
# twitter-roberta-base-mar2022-finetuned-emotion
This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base-mar2022](https://huggingface.co/cardiffnlp/twitter-roberta-base-mar2022) on the tweet_eval dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5146
- Accuracy: 0.8191
- F1: 0.8171
## 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: 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: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8945 | 1.0 | 102 | 0.5831 | 0.7995 | 0.7887 |
| 0.5176 | 2.0 | 204 | 0.5266 | 0.8235 | 0.8200 |
### Framework versions
- Transformers 4.19.3
- Pytorch 1.11.0+cu102
- Datasets 2.2.2
- Tokenizers 0.12.1
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