| import gradio as gr |
| import onnxruntime as rt |
| from transformers import AutoTokenizer |
| import torch, json |
|
|
| tokenizer = AutoTokenizer.from_pretrained("distilroberta-base") |
|
|
|
|
| with open("tag_types_encoded.json", "r") as fp: |
| encode_tag_types = json.load(fp) |
|
|
| tags = list(encode_tag_types.keys()) |
|
|
| inf_session = rt.InferenceSession('question-classifier-quantized.onnx') |
| input_name = inf_session.get_inputs()[0].name |
| output_name = inf_session.get_outputs()[0].name |
|
|
| def classify_question_tags(description): |
| input_ids = tokenizer(description)['input_ids'][:512] |
| logits = inf_session.run([output_name], {input_name: [input_ids]})[0] |
| logits = torch.FloatTensor(logits) |
| probs = torch.sigmoid(logits)[0] |
| |
| return dict(zip(tags, map(float, probs))) |
|
|
| demo = gr.Interface( |
| fn=classify_question_tags, |
| inputs=gr.Textbox(lines=8, placeholder="Enter your question here..."), |
| outputs=gr.Label(num_top_classes=5), |
| examples = [ |
| "I want to develop a machine learning model that predicts the correct medicine dosage required to keep a specific lab value within the target range of 5 to 7. I also have several other predictor variables available. I am unsure which machine learning algorithm would be most suitable for deployment and use with future patients. Additionally, should I define the outcome as binary (1 if the value is between 5 and 7, and 0 otherwise), or is there a better approach?", |
| "What is the best way to evaluate performance of Generative Adverserial Network (GAN)? Perhaps measuring the distance between two distributions or maybe something else?", |
| "Suppose that I have a file which has thousands of skills starting from A-Z. Now, I would like to create a model that can group similar skills together (example neural network and SVM can group together). I know that I can use NLP for this problem, but I'm not sure about the algorithm that I can use to get the best result." |
| ], |
| ) |
| demo.launch(inline=False) |
