Spaces:
Sleeping
Sleeping
File size: 6,177 Bytes
4d6e8c2 32590b1 a66df45 b134905 a66df45 4d6e8c2 a66df45 4d6e8c2 70f5f26 1c33274 70f5f26 f5ac2a0 93741cc f5ac2a0 93741cc f5ac2a0 93741cc f5ac2a0 93741cc f5ac2a0 93741cc f5ac2a0 1c33274 70f5f26 4d6e8c2 70f5f26 4d6e8c2 76fccaf 4d6e8c2 70f5f26 4d6e8c2 92fa037 a66df45 f5ac2a0 a66df45 92fa037 a66df45 93741cc 92fa037 a66df45 70f5f26 4d6e8c2 5518620 4d6e8c2 70f5f26 4d6e8c2 1c33274 4d6e8c2 5518620 4d6e8c2 92fa037 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 |
from fastapi import APIRouter
from datetime import datetime
from datasets import load_dataset
from sklearn.metrics import accuracy_score
import random
from transformers import pipeline, AutoConfig
import os
from concurrent.futures import ThreadPoolExecutor
from typing import List, Dict, Tuple
import numpy as np
import torch
from .utils.evaluation import TextEvaluationRequest
from .utils.emissions import tracker, clean_emissions_data, get_space_info
# Disable torch compile
os.environ["TORCH_COMPILE_DISABLE"] = "1"
router = APIRouter()
DESCRIPTION = "Random Baseline"
ROUTE = "/text"
class TextClassifier:
def __init__(self):
self.config = AutoConfig.from_pretrained("camillebrl/ModernBERT-envclaims-overfit")
self.label2id = self.config.label2id
self.classifier = pipeline(
"text-classification",
"camillebrl/ModernBERT-envclaims-overfit",
device="cpu",
batch_size=16
)
def process_batch(self, batch: List[str], batch_idx: int) -> Tuple[List[int], int]:
"""
Process a batch of texts and return their predictions along with batch index
Args:
batch: List of texts to process
batch_idx: Index of the current batch
Returns:
Tuple containing list of predictions and batch index
"""
try:
print(f"Processing batch {batch_idx} with {len(batch)} items")
batch_preds = self.classifier(list(batch))
predictions = [self.label2id[pred[0]["label"]] for pred in batch_preds]
print(f"Completed batch {batch_idx} with {len(predictions)} predictions")
return predictions, batch_idx
except Exception as e:
print(f"Error in batch {batch_idx}: {str(e)}")
return [], batch_idx
@router.post(ROUTE, tags=["Text Task"],
description=DESCRIPTION)
async def evaluate_text(request: TextEvaluationRequest):
"""
Evaluate text classification for climate disinformation detection.
Current Model: Random Baseline
- Makes random predictions from the label space (0-7)
- Used as a baseline for comparison
"""
# 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"]
test_dataset = dataset["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.
#--------------------------------------------------------------------------------------------
true_labels = test_dataset["label"]
# Initialize the model once
classifier = TextClassifier()
# Prepare batches
batch_size = 32
quotes = test_dataset["quote"]
num_batches = len(quotes) // batch_size + (1 if len(quotes) % batch_size != 0 else 0)
batches = [
quotes[i * batch_size:(i + 1) * batch_size]
for i in range(num_batches)
]
# Initialize batch_results before parallel processing
batch_results = [[] for _ in range(num_batches)]
# Process batches in parallel
max_workers = min(os.cpu_count(), 4) # Limit to 4 workers or CPU count
print(f"Processing with {max_workers} workers")
with ThreadPoolExecutor(max_workers=max_workers) as executor:
# Submit all batches for processing
future_to_batch = {
executor.submit(
classifier.process_batch,
batch,
idx
): idx for idx, batch in enumerate(batches)
}
# Collect results in order
for future in future_to_batch:
batch_idx = future_to_batch[future]
try:
predictions, idx = future.result()
batch_results[idx] = predictions
print(f"Stored results for batch {idx}")
except Exception as e:
print(f"Failed to get results for batch {batch_idx}: {e}")
batch_results[batch_idx] = []
# Flatten predictions while maintaining order
predictions = [pred for batch_preds in batch_results for pred in batch_preds]
print(f"Total predictions collected: {len(predictions)}")
#--------------------------------------------------------------------------------------------
# YOUR MODEL INFERENCE STOPS HERE
#--------------------------------------------------------------------------------------------
# Stop tracking emissions
emissions_data = tracker.stop_task()
# Calculate accuracy
accuracy = accuracy_score(true_labels, predictions)
print("accuracy : ", accuracy)
# 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
}
}
print("results : ", results)
return results |