Update README.md

README.md

@@ -1,20 +1,36 @@
----
-license: mit
----
-### Dataset used 
-[Fake and real news dataset](https://www.kaggle.com/datasets/clmentbisaillon/fake-and-real-news-dataset)
-
-### Labels
-Fake news: 1 </br>
-Real news: 0 
-
-### Performance on test data 
-```json
-'test/accuracy': 0.9977836608886719,
-'test/aucroc': 0.9999998807907104,
-'test/f1': 0.9976308941841125,
-'test/loss': 0.00828308891505003
-```
-
-### Run can be tracked here
+---
+license: mit
+---
+### Dataset used 
+[Fake and real news dataset](https://www.kaggle.com/datasets/clmentbisaillon/fake-and-real-news-dataset)
+
+### Labels
+Fake news: 1 </br>
+Real news: 0 
+
+### Usage 
+```python
+from transformers import AutoModelForSequenceClassification, AutoTokenizer, AutoConfig
+import torch
+
+config = AutoConfig.from_pretrained("bhavitvyamalik/fake-news_xtremedistil-l6-h256-uncased")
+model = AutoModelForSequenceClassification.from_pretrained("bhavitvyamalik/fake-news_xtremedistil-l6-h256-uncased", config=config)
+tokenizer = AutoTokenizer.from_pretrained("microsoft/xtremedistil-l6-h256-uncased", usefast=True)
+
+text = "According to reports by Fox News, Biden is the President of the USA"
+encode = tokenizer(text, max_length=512, truncation=True, padding="max_length", return_tensors="pt")
+
+output = model(**encode)
+print(torch.argmax(output["logits"]))
+```
+
+### Performance on test data 
+```json
+'test/accuracy': 0.9977836608886719,
+'test/aucroc': 0.9999998807907104,
+'test/f1': 0.9976308941841125,
+'test/loss': 0.00828308891505003
+```
+
+### Run can be tracked here
 [Wandb project for Fake news classifier](https://wandb.ai/bhavitvya/Fake%20news%20classifier?workspace=user-bhavitvya)