| --- |
| license: apache-2.0 |
| language: |
| - en |
| - multilingual |
| pipeline_tag: token-classification |
| tags: |
| - gliner |
| - ner |
| - token-classification |
| - social-media |
| - username-extraction |
| library_name: gliner |
| base_model: urchade/gliner_small-v2.1 |
| --- |
| |
| # HandleAtlas-166m |
|
|
| A fine-tuned [GLiNER small v2.1](https://huggingface.co/urchade/gliner_small-v2.1) (~166M params) |
| for extracting social-media handles from short bios. Built on Twitter/X bios but the |
| patterns generalize to other platforms. |
|
|
| ## Labels |
|
|
| - `instagram_username` |
| - `snapchat_username` |
| - `youtube_username` |
| - `twitch_username` |
| - `tiktok_username` |
| - `discord_username` |
| - `x_username` |
| - `cashapp_username` |
| - `onlyfans_username` |
| - `tumblr_username` |
| - `github_username` |
| - `kofi_username` |
| - `patreon_username` |
| - `roblox_username` |
| - `generic_username` |
|
|
| `generic_username` is a fallback for handle-shaped strings without a clear platform |
| indicator. |
|
|
| ## Usage |
|
|
| ```python |
| from gliner import GLiNER |
| |
| model = GLiNER.from_pretrained("LumeData/HandleAtlas-166m") |
| |
| labels = ['instagram_username', 'snapchat_username', 'youtube_username', 'twitch_username', 'tiktok_username', 'discord_username', 'x_username', 'cashapp_username', 'onlyfans_username', 'tumblr_username', 'github_username', 'kofi_username', 'patreon_username', 'roblox_username', 'generic_username'] |
| |
| text = "Insta: foodgrammer | Snap: chefchef | DC: gamer420 | $cashtag" |
| for ent in model.predict_entities(text, labels, threshold=0.5): |
| print(f"{ent['text']!r} -> {ent['label']} ({ent['score']:.2f})") |
| ``` |
|
|
| ## Training |
|
|
| - Base: `urchade/gliner_small-v2.1` |
| - Real data: ~1,000 hand-labeled Twitter bios |
| - Synthetic data: ~2,200 generated bios (template-based + IG→Discord text rewriting |
| for the discord_username class) |
| - Case augmentation: each training record is emitted in original + fully-lowercased |
| form so the model is robust to casing of platform prefixes (`Dc:`/`dc:`/`DC:` etc.) |
| - 5 epochs, batch 4 × grad-accum 2, lr 5e-6 (encoder) / 1e-5 (heads), cosine schedule |
| |
| ## Eval |
| |
| On a 100-record held-out slice of real Twitter bios: |
| |
| | metric | value | |
| |-----------|-------| |
| | precision | 0.849 | |
| | recall | 0.929 | |
| | F1 | 0.887 | |
| |
| Strong per-label F1 on instagram (0.95), youtube (1.00), tiktok (1.00), twitch (1.00), |
| onlyfans (1.00), generic (0.88), cashapp (0.86), snapchat (0.80). |
| |
| ## Recommended thresholds |
| |
| - Default: `threshold=0.5` |
| - For `generic_username`, bump to `0.65` to reduce false positives; it's the |
| catch-all label and over-fires at the default threshold. |
|
|
| ## Limitations |
|
|
| - Trained on patterns common in Twitter/X bios; performance on other domains |
| (LinkedIn-style, Reddit, forum sigs) will be lower. |
| - `discord_invite` is not predicted — invite codes will be classified as |
| `discord_username` or skipped. |
| - Multi-line bios with many handles can occasionally confuse adjacent URL labels |
| (e.g., `patreon.com/x | github.com/x` chains). |
|
|
