Spaces:
Runtime error
Runtime error
File size: 4,266 Bytes
4d6e8c2 712302e 23e47d5 c200604 712302e 0b492aa 712302e 0b492aa cb4572b 0b492aa cb4572b 0b492aa cb4572b 0b492aa cb4572b 0b492aa 712302e 4d6e8c2 712302e 1c33274 70f5f26 1c33274 70f5f26 4d6e8c2 70f5f26 4d6e8c2 70f5f26 712302e 0b492aa 975e4ac 23e47d5 0b492aa 975e4ac 4d6e8c2 712302e 70f5f26 4d6e8c2 70f5f26 4d6e8c2 1c33274 4d6e8c2 |
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 |
from fastapi import APIRouter
from datetime import datetime
from datasets import load_dataset
from sklearn.metrics import accuracy_score
from skops import hub_utils
import skops.io as sio
import skops
from huggingface_hub import hf_hub_download
import joblib
REPO_ID = "kantundpeterpan/frugal-ai-toy"
FILENAME = "tfidf_rf.skops"
# import nltk
# from nltk.tokenize import WordPunctTokenizer
# from nltk.stem import WordNetLemmatizer
# from nltk.corpus import stopwords
# import string
# nltk.download('stopwords')
# stop = set(stopwords.words('english') + list(string.punctuation))
# def tokenize_quote(r):
# tokens = nltk.word_tokenize(r.lower())
# cleaned = [word for word in tokens if word not in stop]
# return cleaned
# def lemmatize_tokens(tokens: list):
# return [lemmatizer.lemmatize(t) for t in tokens]
# def lemmatize_X(X):
# return X.quote.apply(tokenize_quote).apply(lemmatize_tokens).apply(lambda x: " ".join(x))
import random
from .utils.evaluation import TextEvaluationRequest
from .utils.emissions import tracker, clean_emissions_data, get_space_info
router = APIRouter()
DESCRIPTION = "tfidf-rf"
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: 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"].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.
#--------------------------------------------------------------------------------------------
#get unknown types
unknown = skops.io.get_untrusted_types(file = hf_hub_download(repo_id=REPO_ID, filename=FILENAME))
#load model
model = sio.load(
hf_hub_download(repo_id=REPO_ID, filename=FILENAME),
trusted=unknown
)
# Make predictions
true_labels = test_dataset["label"]
predictions = [
LABEL_MAPPING[r] for r in model.predict(test_dataset)
]
#--------------------------------------------------------------------------------------------
# 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 |