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---
license: apache-2.0
datasets:
- G11/climate_adaptation_abstracts
- pierre-pessarossi/wikipedia-climate-data
- rlacombe/ClimateX
language:
- en
base_model:
- google-bert/bert-base-uncased
pipeline_tag: text-classification
---
## Social Media Style Classifier for Climate Change Text
This model is a fine-tuned bert-base-uncased on a binary classification task to determine whether an English text about Climate Change is written in a social media style.
Social media texts were gathered from [ClimaConvo](https://github.com/shucoll/ClimaConvo) and [DEBAGREEMENT](https://datasets-benchmarks-proceedings.neurips.cc/paper_files/paper/2021/hash/6f3ef77ac0e3619e98159e9b6febf557-Abstract-round2.html).
Non-social media texts were gathered from diverse sources including article abstracts (G11/climate_adaptation_abstracts), Wikipedia articles (pierre-pessarossi/wikipedia-climate-data), and IPCC reports (rlacombe/ClimateX).
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 V100-16GB 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
```python
from transformers import AutoModelForSequenceClassification, AutoTokenizer, TextClassificationPipeline
model_name = "rabuahmad/cc-tweets-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 | 0.99747 |
| Precision| 1.0 |
| Recall | 0.99493 |
| F1 | 0.99746 | |