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---
datasets:
- cardiffnlp/tweet_topic_single
metrics:
- f1
- accuracy
model-index:
- name: cardiffnlp/twitter-roberta-base-dec2021-topic-single
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: cardiffnlp/tweet_topic_single
type: cardiffnlp/tweet_topic_single
split: test_2021
metrics:
- name: Micro F1 (cardiffnlp/tweet_topic_single)
type: micro_f1_cardiffnlp/tweet_topic_single
value: 0.896042528056704
- name: Macro F1 (cardiffnlp/tweet_topic_single)
type: micro_f1_cardiffnlp/tweet_topic_single
value: 0.7861641383871055
- name: Accuracy (cardiffnlp/tweet_topic_single)
type: accuracy_cardiffnlp/tweet_topic_single
value: 0.896042528056704
pipeline_tag: text-classification
widget:
- text: Get the all-analog Classic Vinyl Edition of "Takin Off" Album from {@herbiehancock@} via {@bluenoterecords@} link below {{URL}}
example_title: "topic_classification 1"
- text: Yes, including Medicare and social security saving👍
example_title: "sentiment 1"
- text: All two of them taste like ass.
example_title: "offensive 1"
- text: If you wanna look like a badass, have drama on social media
example_title: "irony 1"
- text: Whoever just unfollowed me you a bitch
example_title: "hate 1"
- text: I love swimming for the same reason I love meditating...the feeling of weightlessness.
example_title: "emotion 1"
- text: Beautiful sunset last night from the pontoon @TupperLakeNY
example_title: "emoji 1"
---
# cardiffnlp/twitter-roberta-base-dec2021-topic-single
This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base-dec2021](https://huggingface.co/cardiffnlp/twitter-roberta-base-dec2021) on the
[`cardiffnlp/tweet_topic_single`](https://huggingface.co/datasets/cardiffnlp/tweet_topic_single)
via [`tweetnlp`](https://github.com/cardiffnlp/tweetnlp).
Training split is `train_all` and parameters have been tuned on the validation split `validation_2021`.
Following metrics are achieved on the test split `test_2021` ([link](https://huggingface.co/cardiffnlp/twitter-roberta-base-dec2021-topic-single/raw/main/metric.json)).
- F1 (micro): 0.896042528056704
- F1 (macro): 0.7861641383871055
- Accuracy: 0.896042528056704
### Usage
Install tweetnlp via pip.
```shell
pip install tweetnlp
```
Load the model in python.
```python
import tweetnlp
model = tweetnlp.Classifier("cardiffnlp/twitter-roberta-base-dec2021-topic-single", max_length=128)
model.predict('Get the all-analog Classic Vinyl Edition of "Takin Off" Album from {@herbiehancock@} via {@bluenoterecords@} link below {{URL}}')
```
### Reference
```
@inproceedings{camacho-collados-etal-2022-tweetnlp,
title={{T}weet{NLP}: {C}utting-{E}dge {N}atural {L}anguage {P}rocessing for {S}ocial {M}edia},
author={Camacho-Collados, Jose and Rezaee, Kiamehr and Riahi, Talayeh and Ushio, Asahi and Loureiro, Daniel and Antypas, Dimosthenis and Boisson, Joanne and Espinosa-Anke, Luis and Liu, Fangyu and Mart{'\i}nez-C{'a}mara, Eugenio and others},
author = "Ushio, Asahi and
Camacho-Collados, Jose",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
month = nov,
year = "2022",
address = "Abu Dhabi, U.A.E.",
publisher = "Association for Computational Linguistics",
}
```
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