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# %%
# ----------------------------------------------------------
# Custom Hugging-Face pipeline for the “bonus” split that refers to the existing models
# Task id : quizbowl-bonus
# Expected input keys : leadin, part, previous_parts ('text' and 'guess')
# Must return : answer, confidence, explanation
# ----------------------------------------------------------
import json_repair
import torch
from datasets import Dataset
from loguru import logger
from torch.nn import functional as F
from tqdm.auto import tqdm
from transformers import (
AutoModelForCausalLM,
Pipeline,
TFAutoModelForCausalLM,
pipeline,
)
from transformers.models.llama.modeling_llama import LlamaForCausalLM
from transformers.pipelines import PIPELINE_REGISTRY
def format_part(number: int, text: str, guess: str) -> str:
return f"\t * Part {number}: {text}\n\t * Model Guess: {guess}"
system_prompt = """
You are a quizbowl player. Given the a leadin and your responses to the previous related parts, provide the answer, a brief (1-2 sentences) explanation to the provided question along with your confidence in the guess.
The answer should be a single word or short phrase, and the explanation should be concise and relevant to the question.
The answer should be formatted in the below JSON format:
{
"answer": str,
"explanation": str,
"confidence": float (0-1 in the steps of 0.01)
"justification": str (optional justification for the confidence score)
}
The confidence should be a float between 0 and 1, representing your confidence in the answer.
"""
user_prompt_template = """
"Leadin: {leadin}
Question: {part}"
What is being asked in the question? Provide a concise answer, a brief explanation, and your confidence in the guess along with justification."""
def prepare_conversation(leadin, part):
messages = [
{
"role": "system",
"content": system_prompt,
},
{
"role": "user",
"content": user_prompt_template.format(leadin=leadin, part=part),
},
]
return messages
def parse_output_text(output_text: str):
try:
start_index = output_text.find("{")
if start_index == -1:
raise ValueError("No JSON object found in the output text.")
output_text = output_text[start_index:]
json_data = json_repair.loads(output_text)
if isinstance(json_data, list):
json_data = json_data[0]
answer = json_data.get("answer", "").strip()
explanation = json_data.get("explanation", "").strip()
confidence = json_data.get("confidence", 0.0)
except Exception as e:
logger.warning(
f"Error parsing JSON: {e.__class__.__name__} - {e}. Got:\n{output_text}"
)
answer, explanation, confidence = "", "", 0.0
try:
confidence = float(confidence)
confidence = max(0.0, min(1.0, confidence))
except ValueError:
logger.warning(f"Invalid confidence value: {confidence}. Defaulting to 0.0.")
confidence = 0.0
return {
"answer": answer,
"explanation": explanation,
"confidence": confidence,
}
def postprocess_response(output_text, scores=None):
model_response = parse_output_text(output_text)
# Compute a confidence score by averaging the max softmax probabilities over generated tokens.
if scores is not None and len(scores) > 0:
probs = [F.softmax(score, dim=-1).max().item() for score in scores]
logit_confidence = float(sum(probs) / len(probs)) if probs else 0.0
model_response["confidence"] = (
model_response["confidence"] + logit_confidence
) / 2
return model_response
class QBBonusPipeline(Pipeline):
def __init__(self, model, tokenizer, **kwargs):
super().__init__(
model=model,
tokenizer=tokenizer,
**kwargs,
)
self.tokenizer.padding_side = "left"
self.tokenizer.pad_token = self.tokenizer.eos_token
def _sanitize_parameters(self, **kwargs):
# No additional parameters needed
return {}, {}, {}
def preprocess(self, inputs):
batch_size = len(inputs["leadin"])
conversations = []
for i in range(batch_size):
conversations.append(
prepare_conversation(inputs["leadin"][i], inputs["part"][i])
)
model_inputs = self.tokenizer.apply_chat_template(
conversations,
add_generation_prompt=True,
tokenize=True,
return_dict=True,
padding=True,
return_tensors="pt",
)
return model_inputs
def _forward(self, model_inputs):
with torch.no_grad():
outputs = self.model.generate(
**model_inputs,
max_new_tokens=256,
return_dict_in_generate=True,
output_scores=True,
)
# Remove the input tokens from the output sequences
# This is necessary because the model generates tokens based on the input context
# and we only want the new tokens generated by the model.
input_length = model_inputs["input_ids"].shape[1]
outputs.sequences = outputs.sequences[:, input_length:]
outputs.scores = torch.stack(outputs.scores, dim=1)
return outputs
def postprocess(self, model_outputs):
output_texts = self.tokenizer.batch_decode(
model_outputs.sequences, skip_special_tokens=True
)
records = []
for output_text in output_texts:
record = postprocess_response(output_text)
records.append(record)
return records
PIPELINE_REGISTRY.register_pipeline(
"quizbowl-bonus",
pipeline_class=QBBonusPipeline,
pt_model=LlamaForCausalLM,
default={
"pt": ("meta-llama/Llama-3.2-3B-Instruct", "main"),
},
type="text",
)
# %%
if __name__ == "__main__":
pipe = pipeline("quizbowl-bonus", device_map="auto", trust_remote_code=True)
examples = [
{
"leadin": "This is a leadin.",
"part": "What is the capital of France?",
},
{
"leadin": "This is another leadin.",
"part": "What is the largest planet in our solar system?",
"previous_parts": [
{"text": "What is the smallest planet?", "guess": "Mercury"},
{"text": "What is the second smallest planet?", "guess": "Mars"},
],
},
{
"leadin": "This is a leadin with no previous parts.",
"part": "What is the chemical symbol for water?",
"previous_parts": [],
},
] * 5
dataset = Dataset.from_list(examples)
print("Dataset size:", len(dataset))
outputs = []
batch_size = 5
for batch in tqdm(dataset.batch(batch_size), desc="Processing batches"):
output = pipe(batch, batch_size=batch_size)
outputs.extend(output)
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