README.md

---
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-20251012-205606-t13

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

> Trial timestamp (UTC): 2025-10-12 20:56:06
>
> **Data case:** `en-es-it`

## Configuration (trial hyperparameters)

Model: Alibaba-NLP/gte-multilingual-base

| Hyperparameter | Value |
|---|---|
| LANGUAGES | en-es-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 | 0.5 |
| TEXT_NORMALIZE | True |

## Dev set results (summary)

| Metric | Value |
|---|---|
| f1_macro_dev_0.5 | 0.7148663237442052 |
| f1_weighted_dev_0.5 | 0.8397177643197989 |
| accuracy_dev_0.5 | 0.821826280623608 |
| f1_macro_dev_best_global | 0.7349841198459766 |
| f1_weighted_dev_best_global | 0.864951679724489 |
| accuracy_dev_best_global | 0.8596881959910914 |
| f1_macro_dev_best_by_lang | 0.7336960726166224 |
| f1_weighted_dev_best_by_lang | 0.8575109332646288 |
| accuracy_dev_best_by_lang | 0.8463251670378619 |
| default_threshold | 0.5 |
| best_threshold_global | 0.6 |
| thresholds_by_lang | {"en": 0.5, "it": 0.55, "es": 0.6} |

### Thresholds
- Default: `0.5`
- Best global: `0.6`
- Best by language: `{
  "en": 0.5,
  "it": 0.55,
  "es": 0.6
}`

## Detailed evaluation

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

 no-recl (0)     0.9499    0.8364    0.8895       385
    recl (1)     0.4273    0.7344    0.5402        64

    accuracy                         0.8218       449
   macro avg     0.6886    0.7854    0.7149       449
weighted avg     0.8754    0.8218    0.8397       449
```

### Classification report @ best global threshold (t=0.60)
```text
              precision    recall  f1-score   support

 no-recl (0)     0.9328    0.9013    0.9168       385
    recl (1)     0.5065    0.6094    0.5532        64

    accuracy                         0.8597       449
   macro avg     0.7196    0.7553    0.7350       449
weighted avg     0.8720    0.8597    0.8650       449
```

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

 no-recl (0)     0.9438    0.8727    0.9069       385
    recl (1)     0.4731    0.6875    0.5605        64

    accuracy                         0.8463       449
   macro avg     0.7085    0.7801    0.7337       449
weighted avg     0.8767    0.8463    0.8575       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 | 0.8117 | 0.6081 | 0.8429 | 0.5925 | 0.6877 | 0.8910 | 0.8117 |
| it | 163 | 0.8773 | 0.8056 | 0.8787 | 0.7987 | 0.8132 | 0.8805 | 0.8773 |
| es | 132 | 0.8485 | 0.7533 | 0.8601 | 0.7255 | 0.8080 | 0.8827 | 0.8485 |


## 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-20251012-205606-t13"
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.