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51620d3 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 | import argparse
import json
import tempfile
from pathlib import Path
import fasttext
import numpy as np
from sklearn.metrics import f1_score, classification_report
from src.models.augment import augment, MAXLEN_TO_WINDOW
from src.models.dataset import deduplicate_positions, flatten_to_examples, split_data
from src.schemas.labels import SENTIMENT_LABELS
MODE = "marker"
LABEL_PREFIX = "__label__"
def _to_fasttext_line(example: dict) -> str:
text = example["seg_a"].replace("\n", " ")
label = SENTIMENT_LABELS.id2label[example["label"]]
return f"{LABEL_PREFIX}{label} {text}"
def _write_fasttext_file(examples: list[dict], path: Path) -> None:
with open(path, "w", encoding="utf-8") as f:
for ex in examples:
f.write(_to_fasttext_line(ex) + "\n")
def prepare_data(
data_path: str = "data/data_augmented_256.jsonl",
val_split: float = 0.1,
test_split: float = 0.1,
seed: int = 42,
) -> tuple[list[dict], list[dict], list[dict]]:
with open(data_path, "r", encoding="utf-8") as f:
samples = [json.loads(line) for line in f]
examples = flatten_to_examples(samples, mode=MODE)
train_ex, val_ex, test_ex = split_data(examples, val_split, test_split, seed)
print(f"Train: {len(train_ex)}, Val: {len(val_ex)}, Test: {len(test_ex)}")
return train_ex, val_ex, test_ex
def train(
train_examples: list[dict],
val_examples: list[dict],
output_dir: str = "models/fasttext",
lr: float = 0.5,
epoch: int = 25,
word_ngrams: int = 2,
dim: int = 100,
min_count: int = 1,
) -> fasttext.FastText._FastText:
output_dir = Path(output_dir)
output_dir.mkdir(parents=True, exist_ok=True)
train_file = output_dir / "train.txt"
_write_fasttext_file(train_examples, train_file)
model = fasttext.train_supervised(
input=str(train_file),
lr=lr,
epoch=epoch,
wordNgrams=word_ngrams,
dim=dim,
minCount=min_count,
loss="softmax",
)
model.save_model(str(output_dir / "model.bin"))
print(f"Model saved to {output_dir / 'model.bin'}")
evaluate(model, val_examples, split_name="val")
return model
def evaluate(
model: fasttext.FastText._FastText,
examples: list[dict],
split_name: str = "test",
) -> float:
sentiments = list(SENTIMENT_LABELS.classes)
true_labels = []
pred_labels = []
for ex in examples:
text = ex["seg_a"].replace("\n", " ")
prediction = model.predict(text)[0][0].replace(LABEL_PREFIX, "")
pred_labels.append(prediction)
true_labels.append(SENTIMENT_LABELS.id2label[ex["label"]])
macro_f1 = f1_score(true_labels, pred_labels, average="macro", labels=sentiments)
print(f"\n{split_name} (per-position) macro F1: {macro_f1:.4f}")
print(classification_report(true_labels, pred_labels, labels=sentiments, digits=4))
return macro_f1
def evaluate_entity_level(
model: fasttext.FastText._FastText,
examples: list[dict],
split_name: str = "test",
) -> float:
sentiments = list(SENTIMENT_LABELS.classes)
entity_preds: dict[tuple, tuple[str, float]] = {}
entity_labels: dict[tuple, str] = {}
for ex in examples:
key = (ex["sample_id"], ex["entity_id"])
text = ex["seg_a"].replace("\n", " ")
labels, probs = model.predict(text)
label = labels[0].replace(LABEL_PREFIX, "")
conf = float(probs[0])
if key not in entity_preds or conf > entity_preds[key][1]:
entity_preds[key] = (label, conf)
entity_labels[key] = SENTIMENT_LABELS.id2label[ex["label"]]
true = [entity_labels[k] for k in entity_preds]
pred = [entity_preds[k][0] for k in entity_preds]
macro_f1 = f1_score(true, pred, average="macro", labels=sentiments)
print(f"\n{split_name} (entity-level) macro F1: {macro_f1:.4f}")
print(classification_report(true, pred, labels=sentiments, digits=4))
return macro_f1
def predict_samples(
model: fasttext.FastText._FastText,
samples: list[dict],
window_words: int = 70,
deduplicate: bool = False,
) -> list[dict]:
augmented = augment(samples, window_words)
if deduplicate:
augmented = deduplicate_positions(augmented)
examples = flatten_to_examples(augmented, mode=MODE)
entity_preds: dict[tuple, tuple[str, float]] = {}
for ex in examples:
key = (ex["sample_id"], ex["entity_id"])
text = ex["seg_a"].replace("\n", " ")
labels, probs = model.predict(text)
label = labels[0].replace(LABEL_PREFIX, "")
conf = float(probs[0])
if key not in entity_preds or conf > entity_preds[key][1]:
entity_preds[key] = (label, conf)
results = []
for s in samples:
entities_out = []
for e in s["entities"]:
key = (s["id"], e["entity_id"])
entities_out.append({
"entity_id": e["entity_id"],
"entity_text": e["entity_text"],
"classification": entity_preds.get(key, ("neutral", 0.0))[0],
})
results.append({"id": s["id"], "entities": entities_out})
return results
def main():
parser = argparse.ArgumentParser(description="fastText baseline for entity sentiment")
parser.add_argument("--data", default="data/data_augmented_256.jsonl")
parser.add_argument("--output-dir", default="models/fasttext")
parser.add_argument("--lr", type=float, default=0.5)
parser.add_argument("--epoch", type=int, default=25)
parser.add_argument("--word-ngrams", type=int, default=2)
parser.add_argument("--dim", type=int, default=100)
parser.add_argument("--val-split", type=float, default=0.1)
parser.add_argument("--test-split", type=float, default=0.1)
parser.add_argument("--seed", type=int, default=42)
args = parser.parse_args()
train_ex, val_ex, test_ex = prepare_data(
args.data, args.val_split, args.test_split, args.seed,
)
model = train(
train_ex, val_ex,
output_dir=args.output_dir,
lr=args.lr,
epoch=args.epoch,
word_ngrams=args.word_ngrams,
dim=args.dim,
)
evaluate(model, test_ex, split_name="test")
evaluate_entity_level(model, test_ex, split_name="test")
if __name__ == "__main__":
main()
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