Hyperparameter	Value
LANGUAGES	es
LR	3e-05
EPOCHS	3
MAX_LENGTH	256
USE_BIO	False
USE_LANG_TOKEN	False
GATED_BIO	False
FOCAL_LOSS	True
FOCAL_GAMMA	2.5
USE_SAMPLER	True
R_DROP	True
R_KL_ALPHA	0.5
TEXT_NORMALIZE	True

Dev set results (summary)

Metric	Value
f1_macro_dev_0.5	0.6826923076923077
f1_weighted_dev_0.5	0.8100233100233101
accuracy_dev_0.5	0.7878787878787878
f1_macro_dev_best_global	0.6840845632229757
f1_weighted_dev_best_global	0.8275374406208071
accuracy_dev_best_global	0.8181818181818182
f1_macro_dev_best_by_lang	0.6840845632229757
f1_weighted_dev_best_by_lang	0.8275374406208071
accuracy_dev_best_by_lang	0.8181818181818182
default_threshold	0.5
best_threshold_global	0.55
thresholds_by_lang	{"es": 0.55}

Thresholds

Default: 0.5
Best global: 0.55
Best by language: { "es": 0.55 }

Detailed evaluation

Classification report @ 0.5

text

              precision    recall  f1-score   support

 no-recl (0)     0.9375    0.8036    0.8654       112
    recl (1)     0.3889    0.7000    0.5000        20

    accuracy                         0.7879       132
   macro avg     0.6632    0.7518    0.6827       132
weighted avg     0.8544    0.7879    0.8100       132

Classification report @ best global threshold (t=0.55)

text

              precision    recall  f1-score   support

 no-recl (0)     0.9151    0.8661    0.8899       112
    recl (1)     0.4231    0.5500    0.4783        20

    accuracy                         0.8182       132
   macro avg     0.6691    0.7080    0.6841       132
weighted avg     0.8405    0.8182    0.8275       132

Classification report @ best per-language thresholds

text

              precision    recall  f1-score   support

 no-recl (0)     0.9151    0.8661    0.8899       112
    recl (1)     0.4231    0.5500    0.4783        20

    accuracy                         0.8182       132
   macro avg     0.6691    0.7080    0.6841       132
weighted avg     0.8405    0.8182    0.8275       132

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

lang	n	acc	f1_macro	f1_weighted	prec_macro	rec_macro	prec_weighted	rec_weighted
es	132	0.8182	0.6841	0.8275	0.6691	0.7080	0.8405	0.8182

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/es-no-bio-20251014-t23"
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.