Spaces:
Sleeping
Sleeping
| import gradio as gr | |
| from transformers import AutoTokenizer, AutoModelForSequenceClassification | |
| from transformers import T5Tokenizer, T5ForConditionalGeneration | |
| import torch | |
| device = torch.device("mps" if torch.backends.mps.is_available() else "cpu") | |
| te_tokenizer = AutoTokenizer.from_pretrained('MoritzLaurer/DeBERTa-v3-base-mnli-fever-anli') | |
| te_model = AutoModelForSequenceClassification.from_pretrained('MoritzLaurer/DeBERTa-v3-base-mnli-fever-anli').to(device) | |
| qa_tokenizer = T5Tokenizer.from_pretrained("google/flan-t5-large") | |
| qa_model = T5ForConditionalGeneration.from_pretrained("google/flan-t5-large", device_map="auto") | |
| def predict(context, intent, multi_class): | |
| input_text = "What is the opposite of " + intent + "?" | |
| input_ids = qa_tokenizer(input_text, return_tensors="pt").input_ids.to(device) | |
| opposite_output = qa_tokenizer.decode(qa_model.generate(input_ids, max_length=2)[0], skip_special_tokens=True) | |
| input_text = "What object is the following describing: " + context | |
| input_ids = qa_tokenizer(input_text, return_tensors="pt").input_ids.to(device) | |
| object_output = qa_tokenizer.decode(qa_model.generate(input_ids, max_length=2)[0], skip_special_tokens=True) | |
| batch = ['The ' + object_output + ' is ' + intent, 'The ' + object_output + ' is ' + opposite_output, 'The ' + object_output + ' is neither ' + intent + ' nor ' + opposite_output] | |
| outputs = [] | |
| normal = 0 | |
| for i, hypothesis in enumerate(batch): | |
| input_ids = te_tokenizer.encode(context, hypothesis, return_tensors='pt').to(device) | |
| # -> [contradiction, neutral, entailment] | |
| logits = te_model(input_ids)[0][0] | |
| if (i == 0): | |
| normal = logits | |
| if (i >= 2): | |
| # -> [contradiction, entailment] | |
| probs = logits[[0,2]].softmax(dim=0) | |
| else: | |
| probs = torch.exp(logits) | |
| outputs.append(probs) | |
| # calculate the stochastic vector for it being neither the positive or negative class | |
| perfect_prob = outputs[2] | |
| # -> [entailment, contradiction] for perfect | |
| # -> [entailment, neutral, contradiction] for positive | |
| outputs[1] = outputs[1].flip(dims=[0]) | |
| # combine the negative and positive class by summing by the opposite of the negative class | |
| aggregated = (outputs[0] + outputs[1])/2 | |
| # multiplying vectors | |
| aggregated[1] = aggregated[1] * perfect_prob[0] | |
| aggregated[0] = aggregated[0] * perfect_prob[1] | |
| aggregated[2] = aggregated[2] * perfect_prob[1] | |
| aggregated = torch.sqrt(aggregated) | |
| # multiple true classes | |
| if (multi_class): | |
| aggregated = torch.sigmoid(aggregated) | |
| normal = torch.sigmoid(normal) | |
| # only one true class | |
| else: | |
| aggregated = aggregated.softmax(dim=0) | |
| normal = normal.softmax(dim=0) | |
| aggregated = aggregated.tolist() | |
| return {"agree": aggregated[0], "neutral": aggregated[1], "disagree": aggregated[2]}, {"agree": normal[0], "neutral": normal[1], "disagree": normal[2]} | |
| examples = [["These are my absolute favorite cargos in my closet. I’m 5’7 and they’re actually long enough for me. I’m 165lbs and ordered an M & it fits nice and loose just how I wanted it. The adjustable waist band is awesome!", "long"], ["I feel strongly about politics in the US", "long"], ["The pants are long", "long"], ["The pants are slightly long", "long"]] | |
| gradio_app = gr.Interface( | |
| predict, | |
| examples=examples, | |
| inputs=[gr.Text(label="Statement"), gr.Text(label="Class"), gr.Checkbox(label="Allow multiple true classes")], | |
| outputs=[gr.Label(num_top_classes=3, label="With Postprocessing"), gr.Label(num_top_classes=3, label="Without Postprocessing")], | |
| title="Intent Analysis", | |
| description="This model predicts whether or not the **_class_** describes the **_object described in the sentence_**. <br /> The two outputs shows what TE would predict with and without the postprocessing. An example edge case for normal TE is shown below. <br /> **_It is recommended that you clone the repository to speed up processing time_**. <br /> Additionally, note the difference between the strength of the probability when going between the last two examples, the former representing a strong opinion and the latter a weaker opinion", | |
| cache_examples=True | |
| ) | |
| gradio_app.launch() |