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license: apache-2.0
library_name: transformers
pipeline_tag: text-classification
language:
- it
tags:
- transformers
- xlm-roberta
- multilingual
- social-media
- text-classification
---
# it-no-bio-20251014-t18
**Slur reclamation binary classifier**
Task: LGBTQ+ reclamation vs non-reclamation use of harmful words on social media text.
> Trial timestamp (UTC): 2025-10-14 10:59:01
>
> **Data case:** `it`
## Configuration (trial hyperparameters)
Model: Alibaba-NLP/gte-multilingual-base
| Hyperparameter | Value |
|---|---|
| LANGUAGES | it |
| LR | 2e-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 | 1.0 |
| TEXT_NORMALIZE | True |
## Dev set results (summary)
| Metric | Value |
|---|---|
| f1_macro_dev_0.5 | 0.8966749265256728 |
| f1_weighted_dev_0.5 | 0.9343820254908982 |
| accuracy_dev_0.5 | 0.9325153374233128 |
| f1_macro_dev_best_global | 0.9161091096242924 |
| f1_weighted_dev_best_global | 0.9495941068930785 |
| accuracy_dev_best_global | 0.950920245398773 |
| f1_macro_dev_best_by_lang | 0.9161091096242924 |
| f1_weighted_dev_best_by_lang | 0.9495941068930785 |
| accuracy_dev_best_by_lang | 0.950920245398773 |
| default_threshold | 0.5 |
| best_threshold_global | 0.65 |
| thresholds_by_lang | {"it": 0.65} |
### Thresholds
- Default: `0.5`
- Best global: `0.65`
- Best by language: `{
"it": 0.65
}`
## Detailed evaluation
### Classification report @ 0.5
```text
precision recall f1-score support
no-recl (0) 0.9764 0.9394 0.9575 132
recl (1) 0.7778 0.9032 0.8358 31
accuracy 0.9325 163
macro avg 0.8771 0.9213 0.8967 163
weighted avg 0.9386 0.9325 0.9344 163
```
### Classification report @ best global threshold (t=0.65)
```text
precision recall f1-score support
no-recl (0) 0.9559 0.9848 0.9701 132
recl (1) 0.9259 0.8065 0.8621 31
accuracy 0.9509 163
macro avg 0.9409 0.8957 0.9161 163
weighted avg 0.9502 0.9509 0.9496 163
```
### Classification report @ best per-language thresholds
```text
precision recall f1-score support
no-recl (0) 0.9559 0.9848 0.9701 132
recl (1) 0.9259 0.8065 0.8621 31
accuracy 0.9509 163
macro avg 0.9409 0.8957 0.9161 163
weighted avg 0.9502 0.9509 0.9496 163
```
## Per-language metrics (at best-by-lang)
| lang | n | acc | f1_macro | f1_weighted | prec_macro | rec_macro | prec_weighted | rec_weighted |
|---|---:|---:|---:|---:|---:|---:|---:|---:|
| it | 163 | 0.9509 | 0.9161 | 0.9496 | 0.9409 | 0.8957 | 0.9502 | 0.9509 |
## 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/it-no-bio-20251014-t18"
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.
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