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from transformers import AutoTokenizer, AutoModelForSequenceClassification, MobileBertTokenizerFast
from fastapi import APIRouter
from datetime import datetime
from datasets import load_dataset
from sklearn.metrics import accuracy_score
import random
from .utils.evaluation import TextEvaluationRequest
from .utils.emissions import tracker, clean_emissions_data, get_space_info
import numpy as np
import torch
router = APIRouter()
DESCRIPTION = "FrugalDisinfoHunter Model"
ROUTE = "/text"
@router.post(ROUTE, tags=["Text Task"],
description=DESCRIPTION)
async def evaluate_text(request: TextEvaluationRequest):
"""
Evaluate text classification for climate disinformation detection.
Current Model: FrugalDisinfoHunter
"""
# Get space info
username, space_url = get_space_info()
# Define the label mapping
LABEL_MAPPING = {
"0_not_relevant": 0,
"1_not_happening": 1,
"2_not_human": 2,
"3_not_bad": 3,
"4_solutions_harmful_unnecessary": 4,
"5_science_unreliable": 5,
"6_proponents_biased": 6,
"7_fossil_fuels_needed": 7
}
# Load and prepare the dataset
dataset = load_dataset(request.dataset_name)
# Convert string labels to integers
dataset = dataset.map(lambda x: {"label": LABEL_MAPPING[x["label"]]})
# Split dataset
train_test = dataset["train"].train_test_split(test_size=request.test_size, seed=request.test_seed)
test_dataset = train_test["test"]
# Start tracking emissions
tracker.start()
tracker.start_task("inference")
#--------------------------------------------------------------------------------------------
# YOUR MODEL INFERENCE CODE HERE
# Update the code below to replace the random baseline by your model inference within the inference pass where the energy consumption and emissions are tracked.
#--------------------------------------------------------------------------------------------
# Model and Tokenizer
# Model and Tokenizer - use the same model name for both
model_name = "Zen0/FrugalDisinfoHunter"
model = AutoModelForSequenceClassification.from_pretrained(
model_name,
num_labels=8,
output_hidden_states=True,
problem_type="single_label_classification"
)
tokenizer = AutoTokenizer.from_pretrained(model_name) # Use the same model_name
# Tokenize the test data
test_texts = test_dataset["quote"]
inputs = tokenizer(test_texts, padding=True, truncation=True, return_tensors="pt", max_length=512)
# Move model and inputs to GPU if available
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)
inputs = {key: val.to(device) for key, val in inputs.items()}
# Run inference on the dataset using the model
with torch.no_grad(): # Disable gradient calculations
outputs = model(**inputs)
logits = outputs.logits
# Get predictions from the logits
predictions = torch.argmax(logits, dim=-1).cpu().numpy()
true_labels = test_dataset['label']
#--------------------------------------------------------------------------------------------
# YOUR MODEL INFERENCE STOPS HERE
#--------------------------------------------------------------------------------------------
# Stop tracking emissions
emissions_data = tracker.stop_task()
# Calculate accuracy
accuracy = accuracy_score(true_labels, predictions)
# Prepare results dictionary
results = {
"username": username,
"space_url": space_url,
"submission_timestamp": datetime.now().isoformat(),
"model_description": DESCRIPTION,
"accuracy": float(accuracy),
"energy_consumed_wh": emissions_data.energy_consumed * 1000,
"emissions_gco2eq": emissions_data.emissions * 1000,
"emissions_data": clean_emissions_data(emissions_data),
"api_route": ROUTE,
"dataset_config": {
"dataset_name": request.dataset_name,
"test_size": request.test_size,
"test_seed": request.test_seed
}
}
return results |