| # tweet-topic-19-multi | |
| This is a RoBERTa-base model trained on ~90m tweets until the end of 2019 (see [here](https://huggingface.co/cardiffnlp/twitter-roberta-base-2019-90m)) and finetuned for multi-label topic classification on a corpus of 11,267 [tweets](https://huggingface.co/datasets/cardiffnlp/tweet_topic_multi). | |
| The original RoBERTa-base model can be found [here](https://huggingface.co/cardiffnlp/twitter-roberta-base-2021-124m) and the original reference paper is [TweetEval](https://github.com/cardiffnlp/tweeteval). This model is suitable for English. | |
| - Reference Papers: [TimeLMs paper](https://arxiv.org/abs/2202.03829), [TweetTopic](https://arxiv.org/abs/2209.09824). | |
| - Git Repo: [TimeLMs official repository](https://github.com/cardiffnlp/timelms). | |
| <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-19-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 | |
| ``` |