|
--- |
|
language: en |
|
widget: |
|
- text: It is great to see athletes promoting awareness for climate change. |
|
datasets: |
|
- cardiffnlp/tweet_topic_multi |
|
license: mit |
|
metrics: |
|
- f1 |
|
- accuracy |
|
pipeline_tag: text-classification |
|
--- |
|
|
|
# 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" |
|
} |
|
``` |
