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SimoneAstarita/october-finetuning-more-variables-sweep-20251012-200025-t05

sourceHugging Faceapache-2.0updated 1y agoView on Hugging Face

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october-finetuning-more-variables-sweep-20251012-200025-t05

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

Trial timestamp (UTC): 2025-10-12 20:00:25 Data case: en-es-it

Configuration (trial hyperparameters)

Model: Alibaba-NLP/gte-multilingual-base

Hyperparameter	Value
LANGUAGES	en-es-it
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	1.0
TEXT_NORMALIZE	True

Dev set results (summary)

Metric	Value
f1macrodev_0.5	0.675386567516525
f1weighteddev_0.5	0.8082213964091853
accuracydev0.5	0.7817371937639198
f1macrodevbestglobal	0.7315184893784421
f1weighteddevbestglobal	0.8708804774663164
accuracydevbest_global	0.8730512249443207
f1macrodevbestby_lang	0.7218964421599621
f1weighteddevbestby_lang	0.8449980403391838
accuracydevbestbylang	0.8285077951002228
default_threshold	0.5
bestthresholdglobal	0.8
thresholdsbylang	{"en": 0.45000000000000007, "it": 0.45000000000000007, "es": 0.8}

Thresholds

—Default: 0.5
—Best global: 0.8
—Best by language: { "en": 0.45000000000000007, "it": 0.45000000000000007, "es": 0.8 }

Detailed evaluation

Classification report @ 0.5

text

              precision    recall  f1-score   support

 no-recl (0)     0.9470    0.7896    0.8612       385
    recl (1)     0.3672    0.7344    0.4896        64

    accuracy                         0.7817       449
   macro avg     0.6571    0.7620    0.6754       449
weighted avg     0.8644    0.7817    0.8082       449

Classification report @ best global threshold (t=0.80)

text

              precision    recall  f1-score   support

 no-recl (0)     0.9205    0.9325    0.9265       385
    recl (1)     0.5593    0.5156    0.5366        64

    accuracy                         0.8731       449
   macro avg     0.7399    0.7240    0.7315       449
weighted avg     0.8690    0.8731    0.8709       449

Classification report @ best per-language thresholds

text

              precision    recall  f1-score   support

 no-recl (0)     0.9503    0.8442    0.8941       385
    recl (1)     0.4393    0.7344    0.5497        64

    accuracy                         0.8285       449
   macro avg     0.6948    0.7893    0.7219       449
weighted avg     0.8774    0.8285    0.8450       449

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

lang	n	acc	f1_macro	f1_weighted	prec_macro	rec_macro	prec_weighted	rec_weighted
en	154	0.7792	0.5492	0.8169	0.5481	0.6001	0.8696	0.7792
it	163	0.8466	0.7947	0.8587	0.7667	0.8683	0.8948	0.8466
es	132	0.8636	0.7631	0.8707	0.7409	0.7964	0.8820	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/october-finetuning-more-variables-sweep-20251012-200025-t05"
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.