Update README.md

README.md

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----
-license: apache-2.0
----
+---
+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 NVIDIA 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 |    |
+| Precision|    |
+| Recall   |    |
+| F1       |    |