# Twitter-roBERTa-base-jun2022_sem_eval_2018_task1

This model was trained on ~7000 tweets in English annotated for 11 emotion categories in [SemEval-2018 Task 1: Affect in Tweets: SubTask 5: Emotion Classification](https://competitions.codalab.org/competitions/17751) (also available on the [Hugging Face Dataset Hub](https://huggingface.co/datasets/sem_eval_2018_task_1)).

The underlying model is a RoBERTa-base model trained on 132.26M tweets until the end of June 2022. Fore more details check out the [model page](https://huggingface.co/cardiffnlp/twitter-roberta-base-jun2022).

To quickly test it locally, use a pipeline:

```python
from transformers import pipeline

pipe = pipeline("text-classification",model="maxpe/twitter-roberta-base-jun2022_sem_eval_2018_task_1")

pipe("I couldn't see any seafood for a year after I went to that restaurant that they send all the tourists to!",top_k=11)
```
{}
transformers pytorch roberta text-classification doi:10.57967/hf/0033 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #roberta #text-classification #doi-10.57967/hf/0033 #autotrain_compatible #endpoints_compatible #region-us 

# Twitter-roBERTa-base-jun2022_sem_eval_2018_task1

This model was trained on ~7000 tweets in English annotated for 11 emotion categories in SemEval-2018 Task 1: Affect in Tweets: SubTask 5: Emotion Classification (also available on the Hugging Face Dataset Hub).

The underlying model is a RoBERTa-base model trained on 132.26M tweets until the end of June 2022. Fore more details check out the model page.

To quickly test it locally, use a pipeline:


# Twitter-roBERTa-base-jun2022_sem_eval_2018_task1

This model was trained on ~7000 tweets in English annotated for 11 emotion categories in SemEval-2018 Task 1: Affect in Tweets: SubTask 5: Emotion Classification (also available on the Hugging Face Dataset Hub).

The underlying model is a RoBERTa-base model trained on 132.26M tweets until the end of June 2022. Fore more details check out the model page.

To quickly test it locally, use a pipeline:
TAGS
#transformers #pytorch #roberta #text-classification #doi-10.57967/hf/0033 #autotrain_compatible #endpoints_compatible #region-us 
 # Twitter-roBERTa-base-jun2022_sem_eval_2018_task1

This model was trained on ~7000 tweets in English annotated for 11 emotion categories in SemEval-2018 Task 1: Affect in Tweets: SubTask 5: Emotion Classification (also available on the Hugging Face Dataset Hub).

The underlying model is a RoBERTa-base model trained on 132.26M tweets until the end of June 2022. Fore more details check out the model page.

To quickly test it locally, use a pipeline:
passage: TAGS
#transformers #pytorch #roberta #text-classification #doi-10.57967/hf/0033 #autotrain_compatible #endpoints_compatible #region-us 
# Twitter-roBERTa-base-jun2022_sem_eval_2018_task1

This model was trained on ~7000 tweets in English annotated for 11 emotion categories in SemEval-2018 Task 1: Affect in Tweets: SubTask 5: Emotion Classification (also available on the Hugging Face Dataset Hub).

The underlying model is a RoBERTa-base model trained on 132.26M tweets until the end of June 2022. Fore more details check out the model page.

To quickly test it locally, use a pipeline: