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	0.7779196124639958
f1_weighted_dev_0.5	0.8572152617820797
accuracy_dev_0.5	0.8440677966101695
f1_macro_dev_best_global	0.8132320354542577
f1_weighted_dev_best_global	0.8923452497840444
accuracy_dev_best_global	0.8915254237288136
f1_macro_dev_best_by_lang	0.8132320354542577
f1_weighted_dev_best_by_lang	0.8923452497840444
accuracy_dev_best_by_lang	0.8915254237288136
default_threshold	0.5
best_threshold_global	0.8
thresholds_by_lang	{"it": 0.75, "es": 0.8}

Thresholds

Default: 0.5
Best global: 0.8
Best by language: { "it": 0.75, "es": 0.8 }

Detailed evaluation

Classification report @ 0.5

          precision    recall  f1-score   support

hate (0)     0.9670    0.8402    0.8991       244
recl (1)     0.5301    0.8627    0.6567        51

accuracy                         0.8441       295

macro avg 0.7486 0.8515 0.7779 295 weighted avg 0.8915 0.8441 0.8572 295

Classification report @ best global threshold (t=0.80)

          precision    recall  f1-score   support

hate (0)     0.9380    0.9303    0.9342       244
recl (1)     0.6792    0.7059    0.6923        51

accuracy                         0.8915       295

macro avg 0.8086 0.8181 0.8132 295 weighted avg 0.8933 0.8915 0.8923 295

Classification report @ best per-language thresholds

          precision    recall  f1-score   support

hate (0)     0.9380    0.9303    0.9342       244
recl (1)     0.6792    0.7059    0.6923        51

accuracy                         0.8915       295

macro avg 0.8086 0.8181 0.8132 295 weighted avg 0.8933 0.8915 0.8923 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 | 0.9571 | 0.9294 | 0.9568 | 0.9350 | 0.9241 | 0.9566 | 0.9571 | | es | 132 | 0.8106 | 0.6527 | 0.8159 | 0.6452 | 0.6625 | 0.8221 | 0.8106 |

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-20251008-183651-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.