| 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 | |
| ``` |