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---
datasets:
- cardiffnlp/tweet_topic_single
metrics:
- f1
- accuracy
model-index:
- name: cardiffnlp/twitter-roberta-base-dec2021-tweet-topic-single-2020
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: cardiffnlp/tweet_topic_single
type: cardiffnlp/tweet_topic_single
args: cardiffnlp/tweet_topic_single
split: test_2021
metrics:
- name: F1
type: f1
value: 0.8777318369757826
- name: F1 (macro)
type: f1_macro
value: 0.7461188384572722
- name: Accuracy
type: accuracy
value: 0.8777318369757826
pipeline_tag: text-classification
widget:
- text: "I'm sure the {@Tampa Bay Lightning@} would’ve rather faced the Flyers but man does their experience versus the Blue Jackets this year and last help them a lot versus this Islanders team. Another meat grinder upcoming for the good guys"
example_title: "Example 1"
- text: "Love to take night time bike rides at the jersey shore. Seaside Heights boardwalk. Beautiful weather. Wishing everyone a safe Labor Day weekend in the US."
example_title: "Example 2"
---
# cardiffnlp/twitter-roberta-base-dec2021-tweet-topic-single-2020
This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base-dec2021](https://huggingface.co/cardiffnlp/twitter-roberta-base-dec2021) on the [tweet_topic_single](https://huggingface.co/datasets/cardiffnlp/tweet_topic_single). This model is fine-tuned on `train_2020` split and validated on `test_2021` split of tweet_topic.
Fine-tuning script can be found [here](https://huggingface.co/datasets/cardiffnlp/tweet_topic_single/blob/main/lm_finetuning.py). It achieves the following results on the test_2021 set:
- F1 (micro): 0.8777318369757826
- F1 (macro): 0.7461188384572722
- Accuracy: 0.8777318369757826
### Usage
```python
from transformers import pipeline
pipe = pipeline("text-classification", "cardiffnlp/twitter-roberta-base-dec2021-tweet-topic-single-2020")
topic = pipe("Love to take night time bike rides at the jersey shore. Seaside Heights boardwalk. Beautiful weather. Wishing everyone a safe Labor Day weekend in the US.")
print(topic)
```
### Reference
```
@inproceedings{dimosthenis-etal-2022-twitter,
title = "{T}witter {T}opic {C}lassification",
author = "Antypas, Dimosthenis and
Ushio, Asahi and
Camacho-Collados, Jose and
Neves, Leonardo and
Silva, Vitor and
Barbieri, Francesco",
booktitle = "Proceedings of the 29th International Conference on Computational Linguistics",
month = oct,
year = "2022",
address = "Gyeongju, Republic of Korea",
publisher = "International Committee on Computational Linguistics"
}
```
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