SimoneAstarita/en-no-bio-20251013-151531-t02
017
october-finetuning-monolingual-en-sweep-20251013-151531-t02
Slur reclamation binary classifier Task: LGBTQ+ reclamation vs non-reclamation use of harmful words on social media text.
Trial timestamp (UTC): 2025-10-13 15:15:31 Data case: enConfiguration (trial hyperparameters)
Model: Alibaba-NLP/gte-multilingual-base
Dev set results (summary)
Thresholds
- Default:
0.5 - Best global:
0.5 - Best by language:
{ "en": 0.5 }
Detailed evaluation
Classification report @ 0.5
precision recall f1-score support
no-recl (0) 0.9286 0.8298 0.8764 141
recl (1) 0.1429 0.3077 0.1951 13
accuracy 0.7857 154
macro avg 0.5357 0.5687 0.5358 154
weighted avg 0.8622 0.7857 0.8189 154Classification report @ best global threshold (t=0.50)
precision recall f1-score support
no-recl (0) 0.9286 0.8298 0.8764 141
recl (1) 0.1429 0.3077 0.1951 13
accuracy 0.7857 154
macro avg 0.5357 0.5687 0.5358 154
weighted avg 0.8622 0.7857 0.8189 154Classification report @ best per-language thresholds
precision recall f1-score support
no-recl (0) 0.9286 0.8298 0.8764 141
recl (1) 0.1429 0.3077 0.1951 13
accuracy 0.7857 154
macro avg 0.5357 0.5687 0.5358 154
weighted avg 0.8622 0.7857 0.8189 154Per-language metrics (at best-by-lang)
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
from transformers import AutoTokenizer, AutoModelForSequenceClassification, AutoConfig
import torch, numpy as np
repo = "SimoneAstarita/october-finetuning-monolingual-en-sweep-20251013-151531-t02"
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.