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README.md

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+---
+language: ar
+library_name: pytorch
+tags:
+- arabic
+- poetry
+- meter
+- classification
+---
+
+# ashaar-meter-classification-pytorch
+
+PyTorch character-level Arabic meter classifier.
+
+## Artifacts
+- `model.safetensors`: model weights
+- `config.json`: architecture + class list
+- `stoi.json`: character-to-id mapping (0 is padding)
+- `label2id.json`: meter label mapping
+- `meta.json`: training metadata
+
+## Notes
+This repo stores only the trained model + metadata (no training arrays).

config.json

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+{
+  "task": "text-classification",
+  "tokenization": "character-level",
+  "max_length": 128,
+  "vocab_size": 46,
+  "num_classes": 17,
+  "classes": [
+    "البسيط",
+    "الخفيف",
+    "الرجز",
+    "الرمل",
+    "السريع",
+    "الطويل",
+    "الكامل",
+    "المتدارك",
+    "المتقارب",
+    "المجتث",
+    "المديد",
+    "المضارع",
+    "المقتضب",
+    "المنسرح",
+    "الهزج",
+    "الوافر",
+    "نثر"
+  ],
+  "emb_dim": 64,
+  "num_heads": 4,
+  "ff_dim": 256,
+  "gru_blocks": 3,
+  "dropout": 0.1
+}

inference.py

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+
+import json, numpy as np, torch
+import torch.nn as nn
+from safetensors.torch import load_file
+
+class TransformerBlock(nn.Module):
+    def __init__(self, dim, heads, ff_dim, dropout):
+        super().__init__()
+        self.mha = nn.MultiheadAttention(dim, heads, dropout=dropout, batch_first=True)
+        self.ln1 = nn.LayerNorm(dim); self.ln2 = nn.LayerNorm(dim)
+        self.drop = nn.Dropout(dropout)
+        self.ffn = nn.Sequential(nn.Linear(dim, ff_dim), nn.ReLU(), nn.Dropout(dropout), nn.Linear(ff_dim, dim))
+    def forward(self, x, key_padding_mask=None):
+        attn_out, _ = self.mha(x, x, x, key_padding_mask=key_padding_mask, need_weights=False)
+        x = self.ln1(x + self.drop(attn_out))
+        ff_out = self.ffn(x)
+        return self.ln2(x + self.drop(ff_out))
+
+class BiGRUResidualBlock(nn.Module):
+    def __init__(self, dim, dropout):
+        super().__init__()
+        self.gru = nn.GRU(dim, dim//2, num_layers=1, batch_first=True, bidirectional=True)
+        self.ln = nn.LayerNorm(dim); self.drop = nn.Dropout(dropout)
+    def forward(self, x):
+        out, _ = self.gru(x)
+        return self.ln(x + self.drop(out))
+
+class MeterModel(nn.Module):
+    def __init__(self, vocab_size, num_classes, T, emb_dim=64, num_heads=4, ff_dim=256, gru_blocks=3, dropout=0.1):
+        super().__init__()
+        self.emb = nn.Embedding(vocab_size, emb_dim, padding_idx=0)
+        self.pos = nn.Embedding(T, emb_dim)
+        self.drop = nn.Dropout(dropout)
+        self.tr = TransformerBlock(emb_dim, num_heads, ff_dim, dropout)
+        self.gru_blocks = nn.ModuleList([BiGRUResidualBlock(emb_dim, dropout) for _ in range(gru_blocks)])
+        self.head = nn.Sequential(nn.Linear(emb_dim, 128), nn.ReLU(), nn.Dropout(dropout), nn.Linear(128, num_classes))
+    def forward(self, x):
+        B, T = x.shape
+        pos = torch.arange(T, device=x.device).unsqueeze(0).expand(B, T)
+        h = self.drop(self.emb(x) + self.pos(pos))
+        pad_mask = (x == 0)
+        h = self.tr(h, key_padding_mask=pad_mask)
+        for blk in self.gru_blocks:
+            h = blk(h)
+        mask = (~pad_mask).float().unsqueeze(-1)
+        pooled = (h * mask).sum(1) / mask.sum(1).clamp_min(1.0)
+        return self.head(pooled)
+
+def load_local(repo_dir="."):
+    cfg = json.load(open(f"{repo_dir}/config.json", "r", encoding="utf-8"))
+    stoi = json.load(open(f"{repo_dir}/stoi.json", "r", encoding="utf-8"))
+    classes = cfg["classes"]
+    model = MeterModel(
+        vocab_size=cfg["vocab_size"], num_classes=cfg["num_classes"], T=cfg["max_length"],
+        emb_dim=cfg["emb_dim"], num_heads=cfg["num_heads"], ff_dim=cfg["ff_dim"],
+        gru_blocks=cfg["gru_blocks"], dropout=cfg["dropout"]
+    )
+    sd = load_file(f"{repo_dir}/model.safetensors")
+    model.load_state_dict(sd)
+    model.eval()
+    return model, stoi, classes, cfg["max_length"]
+
+def encode(text, stoi, max_len):
+    ids = np.zeros((max_len,), dtype=np.int64)
+    for i, ch in enumerate(text[:max_len]):
+        ids[i] = stoi.get(ch, 0)
+    return torch.from_numpy(ids).unsqueeze(0)
+
+@torch.no_grad()
+def predict(text, model, stoi, classes, max_len, topk=5):
+    x = encode(text, stoi, max_len)
+    logits = model(x)[0]
+    probs = torch.softmax(logits, dim=-1)
+    topv, topi = torch.topk(probs, k=min(topk, probs.numel()))
+    pred = classes[int(topi[0])]
+    top = [(classes[int(i)], float(v)) for v, i in zip(topv, topi)]
+    return pred, top

label2id.json

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+{
+  "البسيط": 0,
+  "الخفيف": 1,
+  "الرجز": 2,
+  "الرمل": 3,
+  "السريع": 4,
+  "الطويل": 5,
+  "الكامل": 6,
+  "المتدارك": 7,
+  "المتقارب": 8,
+  "المجتث": 9,
+  "المديد": 10,
+  "المضارع": 11,
+  "المقتضب": 12,
+  "المنسرح": 13,
+  "الهزج": 14,
+  "الوافر": 15,
+  "نثر": 16
+}

meta.json

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+{
+  "N": 1657003,
+  "T": 128,
+  "vocab_size": 46,
+  "num_classes": 17,
+  "test_samples": 163917,
+  "classes": [
+    "البسيط",
+    "الخفيف",
+    "الرجز",
+    "الرمل",
+    "السريع",
+    "الطويل",
+    "الكامل",
+    "المتدارك",
+    "المتقارب",
+    "المجتث",
+    "المديد",
+    "المضارع",
+    "المقتضب",
+    "المنسرح",
+    "الهزج",
+    "الوافر",
+    "نثر"
+  ]
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:b4d99dab2a1e84c6d190fa9e4b3ebe198d7400401e3de42bddc833d6bd5a8b54
+size 518076

stoi.json

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+{" ": 1, "ء": 2, "آ": 3, "أ": 4, "ؤ": 5, "إ": 6, "ئ": 7, "ا": 8, "ب": 9, "ة": 10, "ت": 11, "ث": 12, "ج": 13, "ح": 14, "خ": 15, "د": 16, "ذ": 17, "ر": 18, "ز": 19, "س": 20, "ش": 21, "ص": 22, "ض": 23, "ط": 24, "ظ": 25, "ع": 26, "غ": 27, "ف": 28, "ق": 29, "ك": 30, "ل": 31, "م": 32, "ن": 33, "ه": 34, "و": 35, "ى": 36, "ي": 37, "ً": 38, "ٌ": 39, "ٍ": 40, "َ": 41, "ُ": 42, "ِ": 43, "ّ": 44, "ْ": 45}