Datasets:
Tasks:
Text Classification
Formats:
parquet
Sub-tasks:
multi-class-classification
Languages:
Chinese
Size:
1K - 10K
Tags:
poetry
chinese
classical-chinese
sentiment-classification
fine-grained-sentiment
multi-class-classification
License:
File size: 2,812 Bytes
464033f | 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 | # -*- coding: utf-8 -*-
"""Baseline evaluation for PoetryMTEB/FSPC (holistic 5-class sentiment).
Metrics: accuracy, macro-F1, micro-F1.
Default probe: TF-IDF + LogisticRegression (no GPU required).
Usage:
python evaluate_fspc.py
python evaluate_fspc.py --repo PoetryMTEB/FSPC --max-features 50000
"""
from __future__ import annotations
import argparse
import json
from collections import Counter
from datasets import load_dataset
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, f1_score, classification_report
from sklearn.pipeline import Pipeline
def main() -> None:
p = argparse.ArgumentParser()
p.add_argument("--repo", default="PoetryMTEB/FSPC")
p.add_argument("--max-features", type=int, default=50000)
p.add_argument("--seed", type=int, default=42)
p.add_argument("--out-json", default="")
args = p.parse_args()
ds = load_dataset(args.repo)
train, test = ds["train"], ds["test"]
x_train = list(train["poem"])
y_train = list(train["label"])
x_test = list(test["poem"])
y_test = list(test["label"])
id2name = {}
for lab, name in zip(train["label"], train["label_name"]):
id2name[int(lab)] = name
target_names = [id2name[i] for i in sorted(id2name)]
clf = Pipeline(
[
(
"tfidf",
TfidfVectorizer(
analyzer="char",
ngram_range=(1, 3),
max_features=args.max_features,
),
),
(
"lr",
LogisticRegression(
max_iter=2000,
random_state=args.seed,
multi_class="multinomial",
),
),
]
)
clf.fit(x_train, y_train)
pred = clf.predict(x_test)
metrics = {
"accuracy": float(accuracy_score(y_test, pred)),
"macro_f1": float(f1_score(y_test, pred, average="macro")),
"micro_f1": float(f1_score(y_test, pred, average="micro")),
"n_train": len(y_train),
"n_test": len(y_test),
"label_counts_test": dict(Counter(int(x) for x in y_test)),
"report": classification_report(
y_test, pred, target_names=target_names, digits=4
),
}
print(json.dumps({k: v for k, v in metrics.items() if k != "report"}, indent=2))
print(metrics["report"])
if args.out_json:
Path = __import__("pathlib").Path
Path(args.out_json).write_text(
json.dumps(metrics, ensure_ascii=False, indent=2), encoding="utf-8"
)
if __name__ == "__main__":
main()
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