Here's an adapted TWIZ intent detection model, trained on the TWIZ dataset, with an extra focus on simplicity!   
It achieves ~85% accuracy on the TWIZ test set, and should be especially useful for the WSDM students @ NOVA.   

I STRONGLY suggest interested students to check `model_code` in the `Files and versions` tab, where all the code used to get to the model (with the exception of actually uploading it here) is laid out nicely (I hope!)   

Here's the contents of `intent-detection-example.ipynb`, if you're just looking to use the model:   

```python
with open("twiz-data/all_intents.json", 'r') as json_in: # all_intents.json can be found in the task-intent-detector/model_code directory
    data = json.load(json_in)

id_to_intent, intent_to_id = dict(), dict()
for i, intent in enumerate(data):
    id_to_intent[i] = intent
    intent_to_id[intent] = i

model = AutoModelForSequenceClassification.from_pretrained("NOVA-vision-language/task-intent-detector", num_labels=len(data), id2label=id_to_intent, label2id=intent_to_id)
tokenizer = AutoTokenizer.from_pretrained("roberta-base") # you could try 'NOVA-vision-language/task-intent-detector', but I'm not sure I configured it correctly

model_in = tokenizer("I really really wanna go to the next step", return_tensors='pt')
with torch.no_grad():
    logits = model(**model_in).logits # grab the predictions out of the model's classification head
    predicted_class_id = logits.argmax().item() # grab the index of the highest scoring output
    print(model.config.id2label[predicted_class_id]) # use the translation table we just created to translate between that id and the actual intent name
```