Create README.md

README.md

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+## Article
+[Medium article](https://medium.com/@shahrukhx01/multi-task-learning-with-transformers-part-1-multi-prediction-heads-b7001cf014bf)
+## Demo Notebook
+[Colab Notebook Multi-task Query classifiers](https://colab.research.google.com/drive/1R7WcLHxDsVvZXPhr5HBgIWa3BlSZKY6p?usp=sharing)
+## Clone the model repo
+```bash 
+git clone https://huggingface.co/shahrukhx01/bert-multitask-query-classifiers
+```
+```python
+%cd bert-multitask-query-classifiers/
+```
+## Load models
+```python
+from multitask_model import BertForSequenceClassification
+from transformers import AutoTokenizer
+import torch
+model = BertForSequenceClassification.from_pretrained(
+        "shahrukhx01/bert-multitask-query-classifiers",
+        task_labels_map={"quora_keyword_pairs": 2, "spaadia_squad_pairs": 2},
+    )
+tokenizer = AutoTokenizer.from_pretrained("shahrukhx01/bert-multitask-query-classifiers")
+```
+## Run inference on both Tasks
+```python
+from multitask_model import BertForSequenceClassification
+from transformers import AutoTokenizer
+import torch
+model = BertForSequenceClassification.from_pretrained(
+        "shahrukhx01/bert-multitask-query-classifiers",
+        task_labels_map={"quora_keyword_pairs": 2, "spaadia_squad_pairs": 2},
+    )
+tokenizer = AutoTokenizer.from_pretrained("shahrukhx01/bert-multitask-query-classifiers")
+
+## Keyword vs Statement/Question Classifier
+input = ["keyword query", "is this a keyword query?"]
+task_name="quora_keyword_pairs"
+sequence = tokenizer(input, padding=True, return_tensors="pt")['input_ids']
+logits = model(sequence, task_name=task_name)[0]
+predictions = torch.argmax(torch.softmax(logits, dim=1).detach().cpu(), axis=1)
+for input, prediction in zip(input, predictions):
+  print(f"task: {task_name}, input: {input} \n prediction=> {prediction}")
+  print()
+```