Spaces:
Build error
Build error
File size: 7,188 Bytes
46b59f3 ab6d9a9 46b59f3 8da1878 46b59f3 ab6d9a9 46b59f3 91224d2 46b59f3 acfc725 46b59f3 c7e36a5 46b59f3 8da1878 46b59f3 8da1878 46b59f3 8da1878 46b59f3 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 |
import gradio as gr
from transformers import pipeline
import nltk
nltk.download('punkt')
from nltk import sent_tokenize
import torch
from transformers import (
pipeline,
AutoModelForSeq2SeqLM,
AutoTokenizer
)
import re
device = [0 if torch.cuda.is_available() else 'cpu'][0]
def _generate(query, context, model, device):
FT_MODEL = AutoModelForSeq2SeqLM.from_pretrained(model).to(device)
FT_MODEL_TOKENIZER = AutoTokenizer.from_pretrained(model)
input_text = "question: " + query + "</s> question_context: " + context
input_tokenized = FT_MODEL_TOKENIZER.encode(input_text, return_tensors='pt', truncation=True, padding='max_length', max_length=1024).to(device)
_tok_count_assessment = FT_MODEL_TOKENIZER.encode(input_text, return_tensors='pt', truncation=True).to(device)
summary_ids = FT_MODEL.generate(input_tokenized,
max_length=30,
min_length=3,
length_penalty=1.0,
num_beams=2,
early_stopping=True,
)
output = [FT_MODEL_TOKENIZER.decode(id, clean_up_tokenization_spaces=True, skip_special_tokens=True) for id in summary_ids]
return str(output[0])
def predict(query, context):
context = context.encode("ascii", "ignore")
context = context.decode()
#Custom1
cust_model_name = "consciousAI/question-answering-roberta-base-s"
cust_question_answerer = pipeline('question-answering', model=cust_model_name, tokenizer=cust_model_name, device=device)
cust_output = cust_question_answerer(question=query, context=context)
cust_answer = cust_output['answer']
cust_answer_span = "[" + str(cust_output['start']) + "," + str(cust_output['end']) + "]"
cust_confidence = cust_output['score']
cust_answer_sentence = [_sent for _sent in sent_tokenize(context) if cust_answer in _sent]
if len(cust_answer_sentence) > 0:
cust_answer_sentence = cust_answer_sentence[0]
else:
cust_answer_sentence = "Failed matching sentence (answer may be split in multiple sentences)"
#Custom3
cust_model_name_3 = "consciousAI/question-answering-roberta-base-s-v2"
cust_question_answerer_3 = pipeline('question-answering', model=cust_model_name_3, tokenizer=cust_model_name_3, device=device)
cust_output_3 = cust_question_answerer_3(question=query, context=context)
cust_answer_3 = cust_output_3['answer']
cust_answer_span_3 = "[" + str(cust_output_3['start']) + "," + str(cust_output_3['end']) + "]"
cust_confidence_3 = cust_output_3['score']
cust_answer_sentence_3 = [_sent for _sent in sent_tokenize(context) if cust_answer_3 in _sent]
if len(cust_answer_sentence_3) > 0:
cust_answer_sentence_3 = cust_answer_sentence_3[0]
else:
cust_answer_sentence_3 = "Failed matching sentence (answer may be split in multiple sentences)"
#Custom2
cust_answer_2 = _generate(query, context, model="consciousAI/question-answering-generative-t5-v1-base-s-q-c", device=device)
cust_answer_sentence_2 = [_sent for _sent in sent_tokenize(context) if cust_answer_2 in _sent]
if len(cust_answer_sentence_2) > 0:
cust_answer_sentence_2 = cust_answer_sentence_2[0]
else:
cust_answer_sentence_2 = "Failed matching sentence (answer may be split in multiple sentences)"
cust_answer_span_2 = re.search(cust_answer_2, contextDefault).span()
return cust_answer, cust_answer_sentence, cust_answer_span, cust_confidence, cust_answer_2, cust_answer_sentence_2, cust_answer_span_2, cust_answer_sentence_3, cust_answer_3, cust_answer_span_3, cust_confidence_3
with gr.Blocks() as demo:
gr.Markdown(value="# Question Answering Encoders vs Generative\n [Question Answering Leveraging Encoders V1](https://huggingface.co/anshoomehra/question-answering-roberta-base-s)\n\n[Question Answering Leveraging Encoders V2](https://huggingface.co/anshoomehra/question-answering-roberta-base-s-v2)\n\n[Generative Question Answering](https://huggingface.co/anshoomehra/question-answering-generative-t5-v1-base-s-q-c)")
with gr.Accordion(variant='compact', label='Input Values'):
with gr.Row(variant='compact'):
queryDefault = "Which company alongside Amazon, Apple, Meta, and Microsoft is considered part of Big Five?"
contextDefault = "Google LLC is an American multinational technology company focusing on search engine technology, online advertising, cloud computing, computer software, quantum computing, e-commerce, artificial intelligence, and consumer electronics. It has been referred to as 'the most powerful company in the world' and one of the world's most valuable brands due to its market dominance, data collection, and technological advantages in the area of artificial intelligence. Its parent company Alphabet is considered one of the Big Five American information technology companies, alongside Amazon, Apple, Meta, and Microsoft."
query = gr.Textbox(queryDefault, label="Query", placeholder="Dummy Query", lines=2)
context = gr.Textbox(contextDefault, label="Context", placeholder="Dummy Context", lines=5, max_lines = 6)
with gr.Accordion(variant='compact', label='Q&A Model(s) Output'):
with gr.Row(variant='compact'):
with gr.Column(variant='compact'):
_predictionM6 = gr.Textbox(label="question-answering-roberta-base-s: Answer Sentence")
_predictionM5 = gr.Textbox(label="question-answering-roberta-base-s: Answer")
_predictionM7 = gr.Textbox(label="question-answering-roberta-base-s: Q&A Answer Span")
_predictionM8 = gr.Textbox(label="question-answering-roberta-base-s: Answer Confidence")
with gr.Column(variant='compact'):
_predictionM12 = gr.Textbox(label="question-answering-roberta-base-s-v2: Answer Sentence")
_predictionM13 = gr.Textbox(label="question-answering-roberta-base-s-v2: Answer")
_predictionM14 = gr.Textbox(label="question-answering-roberta-base-s-v2: Q&A Answer Span")
_predictionM15 = gr.Textbox(label="question-answering-roberta-base-s-v2: Answer Confidence")
with gr.Column(variant='compact'):
_predictionM10 = gr.Textbox(label="question-answering-generative-t5-v1-base-s-q-c: Sentence")
_predictionM9 = gr.Textbox(label="question-answering-generative-t5-v1-base-s-q-c: Answer")
_predictionM11 = gr.Textbox(label="question-answering-generative-t5-v1-base-s-q-c: Answer Span")
with gr.Row():
gen_btn = gr.Button("Generate Answers")
gen_btn.click(fn=predict,
inputs=[query, context],
outputs=[_predictionM5, _predictionM6, _predictionM7, _predictionM8, _predictionM9, _predictionM10, _predictionM11, _predictionM12, _predictionM13, _predictionM14, _predictionM15]
)
demo.launch(show_error=True) |