Tweet Style Classifier
This model is a fine-tuned bert-base-uncased on a binary classification task to determine whether an English text is a tweet or not.
Tweet texts were gathered from ClimaConvo (https://github.com/shucoll/ClimaConvo) and Sentiment140 (stanfordnlp/sentiment140).
Non-tweet texts were gathered from diverse sources including News article descriptions (heegyu/news-category-dataset), academic papers (gfissore/arxiv-abstracts-2021), emails (snoop2head/enron_aeslc_emails), books (bookcorpus/bookcorpus), and Wikipedoa articles (wikimedia/wikipedia).
The dataset contained about 60K instances, with a 50/50 distribution between the two classes. It was shuffled with a random seed of 42 and split into 80/20 for training/testing. The NVIDIA RTX A6000 GPU was used for training three epochs with a batch size of 8. Other hyperparameters were default values from the HuggingFace Trainer.
The model was trained in order to evaluate a text style transfer task, converting formal-language texts to tweets.
How to use
from transformers import AutoModelForSequenceClassification, AutoTokenizer, TextClassificationPipeline
model_name = "rabuahmad/tweet-style-classifier"
model = AutoModelForSequenceClassification.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name, max_len=512)
classifier = TextClassificationPipeline(model=model, tokenizer=tokenizer, truncation=True, max_length=512)
text = "Yesterday was a great day!"
result = classifier(text)
Label 1 indicates that the text is predicted to be a tweet.
Evaluation
Evaluation results on the test set:
Metric | Score |
---|---|
Accuracy | 0.99312 |
Precision | 0.99251 |
Recall | 0.99397 |
F1 | 0.99324 |
- Downloads last month
- 11
Model tree for rabuahmad/tweet-style-classifier
Base model
google-bert/bert-base-uncased