Hyperparameter	Value
LANGUAGES	en
LR	2e-05
EPOCHS	3
MAX_LENGTH	256
USE_BIO	False
USE_LANG_TOKEN	False
GATED_BIO	False
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	0.36859653075016896
f1_weighted_dev_0.5	0.5015011570992648
accuracy_dev_0.5	0.4090909090909091
f1_macro_dev_best_global	0.5737445630684065
f1_weighted_dev_best_global	0.8659189762392608
accuracy_dev_best_global	0.8636363636363636
f1_macro_dev_best_by_lang	0.5737445630684065
f1_weighted_dev_best_by_lang	0.8659189762392608
accuracy_dev_best_by_lang	0.8636363636363636
default_threshold	0.5
best_threshold_global	0.85
thresholds_by_lang	{"en": 0.85}

Thresholds

Default: 0.5
Best global: 0.85
Best by language: { "en": 0.85 }

Detailed evaluation

Classification report @ 0.5

text

              precision    recall  f1-score   support

 no-recl (0)     0.9808    0.3617    0.5285       141
    recl (1)     0.1176    0.9231    0.2087        13

    accuracy                         0.4091       154
   macro avg     0.5492    0.6424    0.3686       154
weighted avg     0.9079    0.4091    0.5015       154

Classification report @ best global threshold (t=0.85)

text

              precision    recall  f1-score   support

 no-recl (0)     0.9286    0.9220    0.9253       141
    recl (1)     0.2143    0.2308    0.2222        13

    accuracy                         0.8636       154
   macro avg     0.5714    0.5764    0.5737       154
weighted avg     0.8683    0.8636    0.8659       154

Classification report @ best per-language thresholds

text

              precision    recall  f1-score   support

 no-recl (0)     0.9286    0.9220    0.9253       141
    recl (1)     0.2143    0.2308    0.2222        13

    accuracy                         0.8636       154
   macro avg     0.5714    0.5764    0.5737       154
weighted avg     0.8683    0.8636    0.8659       154

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

lang	n	acc	f1_macro	f1_weighted	prec_macro	rec_macro	prec_weighted	rec_weighted
en	154	0.8636	0.5737	0.8659	0.5714	0.5764	0.8683	0.8636

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-20251010-151823-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.