Modelpublic

SimoneAstarita/en-no-bio-20251013-162916-t16

sourceHugging Faceapache-2.0updated 1y agoView on Hugging Face

0likes14downloads

Model Card

en-no-bio-sweep-20251013-162916-t16

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

Trial timestamp (UTC): 2025-10-13 16:29:16 Data case: en

Configuration (trial hyperparameters)

Model: Alibaba-NLP/gte-multilingual-base

Hyperparameter	Value
LANGUAGES	en
LR	1e-05
EPOCHS	5
MAX_LENGTH	256
USE_BIO	False
USELANGTOKEN	False
GATED_BIO	False
FOCAL_LOSS	True
FOCAL_GAMMA	1.5
USE_SAMPLER	True
R_DROP	True
RKLALPHA	0.5
TEXT_NORMALIZE	True

Dev set results (summary)

Metric	Value
f1macrodev_0.5	0.42587719298245613
f1weighteddev_0.5	0.6551777170198223
accuracydev0.5	0.5584415584415584
f1macrodevbestglobal	0.5141225961538461
f1weighteddevbestglobal	0.7816051136363636
accuracydevbest_global	0.7272727272727273
f1macrodevbestby_lang	0.5141225961538461
f1weighteddevbestby_lang	0.7816051136363636
accuracydevbestbylang	0.7272727272727273
default_threshold	0.5
bestthresholdglobal	0.7000000000000001
thresholdsbylang	{"en": 0.7000000000000001}

Thresholds

—Default: 0.5
—Best global: 0.7000000000000001
—Best by language: { "en": 0.7000000000000001 }

Detailed evaluation

Classification report @ 0.5

text

              precision    recall  f1-score   support

 no-recl (0)     0.9195    0.5674    0.7018       141
    recl (1)     0.0896    0.4615    0.1500        13

    accuracy                         0.5584       154
   macro avg     0.5045    0.5145    0.4259       154
weighted avg     0.8495    0.5584    0.6552       154

Classification report @ best global threshold (t=0.70)

text

              precision    recall  f1-score   support

 no-recl (0)     0.9304    0.7589    0.8359       141
    recl (1)     0.1282    0.3846    0.1923        13

    accuracy                         0.7273       154
   macro avg     0.5293    0.5717    0.5141       154
weighted avg     0.8627    0.7273    0.7816       154

Classification report @ best per-language thresholds

text

              precision    recall  f1-score   support

 no-recl (0)     0.9304    0.7589    0.8359       141
    recl (1)     0.1282    0.3846    0.1923        13

    accuracy                         0.7273       154
   macro avg     0.5293    0.5717    0.5141       154
weighted avg     0.8627    0.7273    0.7816       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	0.7273	0.5141	0.7816	0.5293	0.5717	0.8627	0.7273

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/en-no-bio-sweep-20251013-162916-t16"
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, @bestglobal, and @bestbylang. config.json: stores thresholds: defaultthreshold, bestthresholdglobal, thresholdsbylang. postprocessing.json: duplicate threshold info for external tools.