---
license: apache-2.0
library_name: transformers
pipeline_tag: text-classification
language:
  - en
  - es
  - it
tags:
  - transformers
  - xlm-roberta
  - multilingual
  - social-media
  - text-classification
---
# october-finetuning-more-variables-sweep-20251012-202343-t08

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

> Trial timestamp (UTC): 2025-10-12 20:23:43
>
> **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 |
| USE_LANG_TOKEN | False |
| GATED_BIO | False |
| FOCAL_LOSS | True |
| FOCAL_GAMMA | 1.5 |
| USE_SAMPLER | True |
| R_DROP | True |
| R_KL_ALPHA | 0.5 |
| TEXT_NORMALIZE | True |

## Dev set results (summary)

| Metric | Value |
|---|---|
| f1_macro_dev_0.5 | 0.6966881607229021 |
| f1_weighted_dev_0.5 | 0.8396412101658748 |
| accuracy_dev_0.5 | 0.8285077951002228 |
| f1_macro_dev_best_global | 0.7184728843416901 |
| f1_weighted_dev_best_global | 0.861020415912312 |
| accuracy_dev_best_global | 0.8596881959910914 |
| f1_macro_dev_best_by_lang | 0.7297459973516311 |
| f1_weighted_dev_best_by_lang | 0.8726099195059952 |
| accuracy_dev_best_by_lang | 0.8775055679287305 |
| default_threshold | 0.5 |
| best_threshold_global | 0.65 |
| thresholds_by_lang | {"en": 0.65, "it": 0.6, "es": 0.8} |

### Thresholds
- Default: `0.5`
- Best global: `0.65`
- Best by language: `{
  "en": 0.65,
  "it": 0.6,
  "es": 0.8
}`

## Detailed evaluation

### Classification report @ 0.5
```text
              precision    recall  f1-score   support

 no-recl (0)     0.9278    0.8675    0.8966       385
    recl (1)     0.4270    0.5938    0.4967        64

    accuracy                         0.8285       449
   macro avg     0.6774    0.7306    0.6967       449
weighted avg     0.8564    0.8285    0.8396       449
```

### Classification report @ best global threshold (t=0.65)
```text
              precision    recall  f1-score   support

 no-recl (0)     0.9215    0.9143    0.9179       385
    recl (1)     0.5075    0.5312    0.5191        64

    accuracy                         0.8597       449
   macro avg     0.7145    0.7228    0.7185       449
weighted avg     0.8625    0.8597    0.8610       449
```

### Classification report @ best per-language thresholds
```text
              precision    recall  f1-score   support

 no-recl (0)     0.9167    0.9429    0.9296       385
    recl (1)     0.5849    0.4844    0.5299        64

    accuracy                         0.8775       449
   macro avg     0.7508    0.7136    0.7297       449
weighted avg     0.8694    0.8775    0.8726       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 | 0.8636 | 0.5429 | 0.8612 | 0.5446 | 0.5415 | 0.8587 | 0.8636 |
| it | 163 | 0.8834 | 0.7980 | 0.8794 | 0.8216 | 0.7799 | 0.8779 | 0.8834 |
| es | 132 | 0.8864 | 0.7530 | 0.8795 | 0.7906 | 0.7277 | 0.8770 | 0.8864 |


## 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-20251012-202343-t08"
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.