Update README.md

README.md

@@ -12,52 +12,52 @@ tags:
 - lightweight
 pipeline_tag: text-classification
 ---
-
-# ThingsLID — Multi-Task Byte-Level Text Classifier
-
+ 
+# Glyph — Multi-Task Byte-Level Text Classifier
+ 
 **4M parameters** · **5 tasks** · **No tokenizer needed** · **Runs on CPU**
-
-ThingsLID is an ultra-compact multi-task text classification model that operates directly on raw UTF-8 bytes. A single shared backbone serves 5 classification heads simultaneously.
-
+ 
+Glyph is an ultra-compact multi-task text classification model that operates directly on raw UTF-8 bytes. A single shared backbone serves 5 classification heads simultaneously.
+ 
 ## Tasks & Performance
-
+ 
 | Task | Labels | Val Accuracy | Description |
 |------|--------|-------------|-------------|
-| `lang_id` | 351 | 94.3% | Language identification |
-| `prog_lang` | 30 | 94.4% | Programming language detection |
-| `spam` | 2 | 96.1% | Spam vs ham classification |
-| `toxic` | 2 | 67.3% | Toxicity detection (multilingual) |
-
-**Average validation accuracy: 88.0%**
-
+| `lang_id` | {n_lang_id} | {acc_lang_id:.1f}% | Language identification |
+| `prog_lang` | {n_prog_lang} | {acc_prog_lang:.1f}% | Programming language detection |
+| `spam` | 2 | {acc_spam:.1f}% | Spam vs ham classification |
+| `toxic` | 2 | {acc_toxic:.1f}% | Toxicity detection (multilingual) |
+ 
+**Average validation accuracy: {avg_acc:.1f}%**
+ 
 ## Architecture
-
-ThingsLID uses a byte-level hybrid architecture inspired by [CommonLingua](https://huggingface.co/PleIAs/CommonLingua):
-
+ 
+Glyph uses a byte-level hybrid architecture inspired by [CommonLingua](https://huggingface.co/PleIAs/CommonLingua):
+ 
 - **Input**: Raw UTF-8 bytes (no tokenizer), padded to 512 bytes
 - **Trigram hash embedding**: Polynomial rolling hash of byte 3-grams → 8192-bucket embedding table
 - **Byte unigram embedding**: Standard embedding for individual bytes
 - **4× Conv1D blocks**: Causal convolutions + BatchNorm + GELU + residual
 - **2× Bidirectional attention**: Multi-head self-attention with RoPE
 - **Global average pooling** → per-task classification heads
-
+ 
 Total: ~4M shared parameters + small per-task linear heads.
-
+ 
 ## Usage
-
+ 
 ```python
 import torch
 from huggingface_hub import hf_hub_download
-
+ 
 # Download and load
-ckpt_path = hf_hub_download("ThingAI/ThingsLID", "model.pt")
+ckpt_path = hf_hub_download("ThingAI/Glyph", "model.pt")
 ckpt = torch.load(ckpt_path, map_location="cpu", weights_only=False)
-
+ 
 # Reconstruct model (see model.py in repo)
 from model import MultiTaskLID
 model = MultiTaskLID(ckpt["task_configs"]).eval()
 model.load_state_dict(ckpt["model"])
-
+ 
 # Predict
 def predict(text, task="lang_id", top_k=3):
     raw = text.encode("utf-8")[:512]
@@ -66,68 +66,68 @@ def predict(text, task="lang_id", top_k=3):
     with torch.no_grad():
         logits = model(inp, task)["logits"][0]
     probs = torch.softmax(logits, dim=-1)
-    idx2label = {i: l for l, i in ckpt["label_maps"][task].items()}
+    idx2label = {{i: l for l, i in ckpt["label_maps"][task].items()}}
     topk = probs.topk(top_k)
     return [(idx2label[topk.indices[i].item()], topk.values[i].item()) for i in range(top_k)]
-
+ 
 # Examples
 print(predict("La pizza napoletana è patrimonio UNESCO", task="lang_id"))
 # [('ita', 0.98), ('cos', 0.003), ...]
-
+ 
 print(predict("def foo(x): return x + 1", task="prog_lang"))
 # [('Python', 0.95), ...]
-
+ 
 print(predict("You won a FREE iPhone!!!", task="spam"))
 # [('spam', 0.99), ...]
 ```
-
+ 
 ## Quick predict script
-
+ 
 ```bash
 python predict.py --text "Ciao, come stai?"
 # lang_id: ita (98.2%)
-
-python predict.py --task prog_lang --text "fn main() { println!("hello"); }"
+ 
+python predict.py --task prog_lang --text "fn main() {{ println!(\"hello\"); }}"
 # prog_lang: Rust (97.1%)
-
+ 
 python predict.py --task spam --text "URGENT: Click here to win!"
 # spam: spam (99.5%)
 ```
-
+ 
 ## Training Data
-
+ 
 | Task | Dataset | Samples |
 |------|---------|---------|
 | Language ID | [PleIAs/CommonLingua-Train](https://huggingface.co/datasets/PleIAs/CommonLingua-Train) | 200K (capped) |
 | Programming Language | [cakiki/rosetta-code](https://huggingface.co/datasets/cakiki/rosetta-code) | ~26K |
 | Spam Detection | [ucirvine/sms_spam](https://huggingface.co/datasets/ucirvine/sms_spam) | 5.5K |
 | Toxicity | [textdetox/multilingual_toxicity_dataset](https://huggingface.co/datasets/textdetox/multilingual_toxicity_dataset) | ~45K |
-
+ 
 ## Key Design Decisions
-
+ 
 - **Byte-level input**: No tokenizer means it works on any language, script, or encoding without preprocessing
 - **Trigram hashing**: +1.2 F1 over unigram-only baseline, acts as regularization via hash collisions
 - **Multi-task learning**: Shared backbone learns universal text representations; per-task heads are tiny (~130K params each)
 - **No attention masking**: Bidirectional attention for classification (not causal)
 - **OneCycleLR**: Fast convergence in few epochs
-
+ 
 ## Limitations
-
+ 
 - Designed for paragraph-level classification (50+ bytes). Short texts (<20 bytes) may be unreliable
 - Toxicity detection accuracy is lower than specialized models (trained on limited multilingual data)
 - Programming language detection trained on Rosetta Code samples which have a specific style
-
+ 
 ## Citation
-
+ 
 ```bibtex
-@misc{thingslid2026,
-  author = {ThingAI},
-  title  = {ThingsLID: Multi-Task Byte-Level Text Classifier},
-  year   = {2026},
-  url    = {https://huggingface.co/ThingAI/ThingsLID}
-}
+@misc{{glyph2026,
+  author = {{ThingAI}},
+  title  = {{Glyph: Multi-Task Byte-Level Text Classifier}},
+  year   = {{2026}},
+  url    = {{https://huggingface.co/ThingAI/Glyph}}
+}}
 ```
-
+ 
 ## License
-
+ 
 Apache 2.0