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--- |
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license: apache-2.0 |
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datasets: |
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- G11/climate_adaptation_abstracts |
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- pierre-pessarossi/wikipedia-climate-data |
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- rlacombe/ClimateX |
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language: |
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- en |
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base_model: |
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- google-bert/bert-base-uncased |
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pipeline_tag: text-classification |
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--- |
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## Social Media Style Classifier for Climate Change Text |
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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. |
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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). |
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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). |
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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. |
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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. |
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The model was trained in order to evaluate a text style transfer task, converting formal-language texts to tweets. |
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### How to use |
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```python |
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from transformers import AutoModelForSequenceClassification, AutoTokenizer, TextClassificationPipeline |
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model_name = "rabuahmad/cc-tweets-classifier" |
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model = AutoModelForSequenceClassification.from_pretrained(model_name) |
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tokenizer = AutoTokenizer.from_pretrained(model_name, max_len=512) |
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classifier = TextClassificationPipeline(model=model, tokenizer=tokenizer, truncation=True, max_length=512) |
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text = "Yesterday was a great day!" |
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result = classifier(text) |
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``` |
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Label 1 indicates that the text is predicted to be a tweet. |
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### Evaluation |
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Evaluation results on the test set: |
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| Metric |Score | |
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|----------|-----------| |
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| Accuracy | 0.99747 | |
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| Precision| 1.0 | |
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| Recall | 0.99493 | |
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| F1 | 0.99746 | |
