Hyperparameter	Value
LANGUAGES	es-it
LR	2e-05
EPOCHS	3
MAX_LENGTH	256
USE_BIO	True
USE_LANG_TOKEN	False
GATED_BIO	True
FOCAL_LOSS	True
FOCAL_GAMMA	1.5
USE_SAMPLER	True
R_DROP	True
R_KL_ALPHA	1.0
TEXT_NORMALIZE	True

Dev set results (summary)

Metric	Value
f1_macro_dev_0.5	0.794306219281985
f1_weighted_dev_0.5	0.8709441410975659
accuracy_dev_0.5	0.8610169491525423
f1_macro_dev_best_global	0.8245942434319188
f1_weighted_dev_best_global	0.9039516171475004
accuracy_dev_best_global	0.9084745762711864
f1_macro_dev_best_by_lang	0.8117021276595744
f1_weighted_dev_best_by_lang	0.8847818247385503
accuracy_dev_best_by_lang	0.8779661016949153
default_threshold	0.5
best_threshold_global	0.85
thresholds_by_lang	{"it": 0.7, "es": 0.6}

Thresholds

Default: 0.5
Best global: 0.85
Best by language: { "it": 0.7, "es": 0.6 }

Detailed evaluation

Classification report @ 0.5

text

              precision    recall  f1-score   support

 no-recl (0)     0.9635    0.8648    0.9114       244
    recl (1)     0.5658    0.8431    0.6772        51

    accuracy                         0.8610       295
   macro avg     0.7646    0.8539    0.7943       295
weighted avg     0.8947    0.8610    0.8709       295

Classification report @ best global threshold (t=0.85)

text

              precision    recall  f1-score   support

 no-recl (0)     0.9255    0.9672    0.9459       244
    recl (1)     0.8000    0.6275    0.7033        51

    accuracy                         0.9085       295
   macro avg     0.8627    0.7973    0.8246       295
weighted avg     0.9038    0.9085    0.9040       295

Classification report @ best per-language thresholds

text

              precision    recall  f1-score   support

 no-recl (0)     0.9602    0.8893    0.9234       244
    recl (1)     0.6087    0.8235    0.7000        51

    accuracy                         0.8780       295
   macro avg     0.7844    0.8564    0.8117       295
weighted avg     0.8994    0.8780    0.8848       295

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

lang	n	acc	f1_macro	f1_weighted	prec_macro	rec_macro	prec_weighted	rec_weighted
it	163	0.9632	0.9417	0.9636	0.9317	0.9526	0.9645	0.9632
es	132	0.7727	0.6600	0.7965	0.6441	0.7223	0.8413	0.7727

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

python

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

repo = "SimoneAstarita/Pride-large-try-sweep-20251008-202520-t00"
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)))

### 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.
report_0.5.txt, report_best.txt — readable classification reports.
postprocessing.json — duplicate threshold info for external tools.