---
language: en
widget:
- text: "It is great to see athletes promoting awareness for climate change."
---

# tweet-topic-21-multi

This model is based on a [TimeLMs](https://github.com/cardiffnlp/timelms) language model trained on ~124M tweets from January 2018 to December 2021 (see [here](https://huggingface.co/cardiffnlp/twitter-roberta-base-2021-124m)), and finetuned for multi-label topic classification on a corpus of 11,267 [tweets](https://huggingface.co/datasets/cardiffnlp/tweet_topic_multi). This model is suitable for English. 

 - Reference Paper: [TweetTopic](https://arxiv.org/abs/2209.09824) (COLING 2022). 

<b>Labels</b>: 


| <span style="font-weight:normal">0: arts_&_culture</span>           | <span style="font-weight:normal">5: fashion_&_style</span>   | <span style="font-weight:normal">10: learning_&_educational</span>  | <span style="font-weight:normal">15: science_&_technology</span>  |
|-----------------------------|---------------------|----------------------------|--------------------------|
| 1: business_&_entrepreneurs | 6: film_tv_&_video  | 11: music                  | 16: sports               |
| 2: celebrity_&_pop_culture  | 7: fitness_&_health | 12: news_&_social_concern  | 17: travel_&_adventure   |
| 3: diaries_&_daily_life     | 8: food_&_dining    | 13: other_hobbies          | 18: youth_&_student_life |
| 4: family                   | 9: gaming           | 14: relationships          |                          |


## Full classification example

```python
from transformers import AutoModelForSequenceClassification, TFAutoModelForSequenceClassification
from transformers import AutoTokenizer
import numpy as np
from scipy.special import expit

    
MODEL = f"cardiffnlp/tweet-topic-21-multi"
tokenizer = AutoTokenizer.from_pretrained(MODEL)

# PT
model = AutoModelForSequenceClassification.from_pretrained(MODEL)
class_mapping = model.config.id2label

text = "It is great to see athletes promoting awareness for climate change."
tokens = tokenizer(text, return_tensors='pt')
output = model(**tokens)

scores = output[0][0].detach().numpy()
scores = expit(scores)
predictions = (scores >= 0.5) * 1


# TF
#tf_model = TFAutoModelForSequenceClassification.from_pretrained(MODEL)
#class_mapping = tf_model.config.id2label
#text = "It is great to see athletes promoting awareness for climate change."
#tokens = tokenizer(text, return_tensors='tf')
#output = tf_model(**tokens)
#scores = output[0][0]
#scores = expit(scores)
#predictions = (scores >= 0.5) * 1

# Map to classes
for i in range(len(predictions)):
  if predictions[i]:
    print(class_mapping[i])

```
Output: 

```
news_&_social_concern
sports
```

### BibTeX entry and citation info

Please cite the [reference paper](https://aclanthology.org/2022.coling-1.299/) if you use this model.

```bibtex
@inproceedings{antypas-etal-2022-twitter,
    title = "{T}witter Topic Classification",
    author = "Antypas, Dimosthenis  and
      Ushio, Asahi  and
      Camacho-Collados, Jose  and
      Silva, Vitor  and
      Neves, Leonardo  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",
    url = "https://aclanthology.org/2022.coling-1.299",
    pages = "3386--3400",
    abstract = "Social media platforms host discussions about a wide variety of topics that arise everyday. Making sense of all the content and organising it into categories is an arduous task. A common way to deal with this issue is relying on topic modeling, but topics discovered using this technique are difficult to interpret and can differ from corpus to corpus. In this paper, we present a new task based on tweet topic classification and release two associated datasets. Given a wide range of topics covering the most important discussion points in social media, we provide training and testing data from recent time periods that can be used to evaluate tweet classification models. Moreover, we perform a quantitative evaluation and analysis of current general- and domain-specific language models on the task, which provide more insights on the challenges and nature of the task.",
}
```