it-no-bio-20251014-t10

Slur reclamation binary classifier
Task: LGBTQ+ reclamation vs non-reclamation use of harmful words on social media text.

Trial timestamp (UTC): 2025-10-14 10:28:36

Data case: it

Configuration (trial hyperparameters)

Model: Alibaba-NLP/gte-multilingual-base

Hyperparameter	Value
LANGUAGES	it
LR	1e-05
EPOCHS	5
MAX_LENGTH	256
USE_BIO	False
USE_LANG_TOKEN	False
GATED_BIO	False
FOCAL_LOSS	True
FOCAL_GAMMA	2.5
USE_SAMPLER	False
R_DROP	True
R_KL_ALPHA	0.5
TEXT_NORMALIZE	True

Dev set results (summary)

Metric	Value
f1_macro_dev_0.5	0.8917858651698956
f1_weighted_dev_0.5	0.932922632145847
accuracy_dev_0.5	0.9325153374233128
f1_macro_dev_best_global	0.8951363870303655
f1_weighted_dev_best_global	0.9369926336163481
accuracy_dev_best_global	0.9386503067484663
f1_macro_dev_best_by_lang	0.8951363870303655
f1_weighted_dev_best_by_lang	0.9369926336163481
accuracy_dev_best_by_lang	0.9386503067484663
default_threshold	0.5
best_threshold_global	0.55
thresholds_by_lang	{"it": 0.55}

Thresholds

• Default: 0.5
• Best global: 0.55
• Best by language: { "it": 0.55 }

Detailed evaluation

Classification report @ 0.5

              precision    recall  f1-score   support

 no-recl (0)     0.9618    0.9545    0.9582       132
    recl (1)     0.8125    0.8387    0.8254        31

    accuracy                         0.9325       163
   macro avg     0.8872    0.8966    0.8918       163
weighted avg     0.9334    0.9325    0.9329       163

Classification report @ best global threshold (t=0.55)

              precision    recall  f1-score   support

 no-recl (0)     0.9485    0.9773    0.9627       132
    recl (1)     0.8889    0.7742    0.8276        31

    accuracy                         0.9387       163
   macro avg     0.9187    0.8757    0.8951       163
weighted avg     0.9372    0.9387    0.9370       163

Classification report @ best per-language thresholds

              precision    recall  f1-score   support

 no-recl (0)     0.9485    0.9773    0.9627       132
    recl (1)     0.8889    0.7742    0.8276        31

    accuracy                         0.9387       163
   macro avg     0.9187    0.8757    0.8951       163
weighted avg     0.9372    0.9387    0.9370       163

Per-language metrics (at best-by-lang)

lang	n	acc	f1_macro	f1_weighted	prec_macro	rec_macro	prec_weighted	rec_weighted
it	163	0.9387	0.8951	0.9370	0.9187	0.8757	0.9372	0.9387

Data

• Train/Dev: private multilingual splits with ~15% stratified Dev (by (lang,label)).
• Source: merged EN/IT/ES data with bios retained (ignored if unused by model).

Usage

from transformers import AutoTokenizer, AutoModelForSequenceClassification, AutoConfig
import torch, numpy as np

repo = "SimoneAstarita/it-no-bio-20251014-t10"
tok = AutoTokenizer.from_pretrained(repo)
cfg = AutoConfig.from_pretrained(repo)
model = AutoModelForSequenceClassification.from_pretrained(repo)

texts = ["example text ..."]
langs = ["en"]

mode = "best_global"  # or "0.5", "by_lang"

enc = tok(texts, truncation=True, padding=True, max_length=256, return_tensors="pt")
with torch.no_grad():
    logits = model(**enc).logits
probs = torch.softmax(logits, dim=-1)[:, 1].cpu().numpy()

if mode == "0.5":
    th = 0.5
    preds = (probs >= th).astype(int)
elif mode == "best_global":
    th = getattr(cfg, "best_threshold_global", 0.5)
    preds = (probs >= th).astype(int)
elif mode == "by_lang":
    th_by_lang = getattr(cfg, "thresholds_by_lang", {})
    preds = np.zeros_like(probs, dtype=int)
    for lg in np.unique(langs):
        t = th_by_lang.get(lg, getattr(cfg, "best_threshold_global", 0.5))
        preds[np.array(langs) == lg] = (probs[np.array(langs) == lg] >= t).astype(int)
print(list(zip(texts, preds, probs)))

Additional files

reports.json: all metrics (macro/weighted/accuracy) for @0.5, @best_global, and @best_by_lang. config.json: stores thresholds: default_threshold, best_threshold_global, thresholds_by_lang. postprocessing.json: duplicate threshold info for external tools.