Scicom-intl
/

multilingual-dynamic-entity-decoder

@@ -1,199 +1,99 @@
 ---
 library_name: transformers
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 ---
 library_name: transformers
+base_model:
+- Qwen/Qwen3-0.6B
 ---
+# Model Overview
+This model is a multilingual Named Entity Recognition (NER) transformer designed for name
+and address entity extraction with Malaysian context.
+It supports the following languages:
+- English
+- Malay
+- Chinese
+- Tamil
+The model is built on top of Qwen3(Qwen3-0.6B) and uses a custom non-causal attention
+mechanism.
+## Predicted Classes
+0 - Non-entity token
+1 - Name entity
+2 - Address entity
+## Transformer Inference Example
+```python
+from transformers import AutoTokenizer, Qwen3ForTokenClassification, AttentionInterface
+from typing import Optional
+def register_fa_attention():
+    from flash_attn import flash_attn_func, flash_attn_varlen_func
+    def custom_attention_forward(
+        module: AttentionInterface,
+        query: torch.Tensor,
+        key: torch.Tensor,
+        value: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        **kwargs,
+    ):
+        cu_seqlens_q = kwargs.get("cu_seqlens_q", None)
+        cu_seqlens_k = kwargs.get("cu_seqlens_k", None)
+        max_seqlen_q = kwargs.get("max_seqlen_q", None)
+        max_seqlen_k = kwargs.get("max_seqlen_k", None)
+        # permute query, key, value to (batch, seq_len, n_heads, head_dim)
+        query_permute = query.permute(0, 2, 1, 3)
+        key_permute = key.permute(0, 2, 1, 3)
+        value_permute = value.permute(0, 2, 1, 3)
+        if cu_seqlens_q is not None and cu_seqlens_k is not None:
+            attn_output = flash_attn_varlen_func(
+                q=query_permute.squeeze(0),
+                k=key_permute.squeeze(0),
+                v=value_permute.squeeze(0),
+                cu_seqlens_q=cu_seqlens_q,
+                cu_seqlens_k=cu_seqlens_k,
+                max_seqlen_q=max_seqlen_q,
+                max_seqlen_k=max_seqlen_k,
+                causal=False,
+            )
+        else:
+            attn_output = flash_attn_func(
+                query_permute, key_permute, value_permute,
+                causal=False,
+            )
+        return attn_output , None
+    AttentionInterface.register("fa_noncausal", custom_attention_forward)
+# Register custom non-causal FA (Feel free to use FA2/FA3), required GPU
+register_fa_attention()
+tokenizer = AutoTokenizer.from_pretrained("Scicom-intl/multilingual-dynamic-entity-decoder")
+model = Qwen3ForTokenClassification.from_pretrained(
+    "Scicom-intl/multilingual-dynamic-entity-decoder",
+    attn_implementation="fa_noncausal",
+    dtype=torch.bfloat16,
+    device_map={"":"cuda:0"}
+)
+text = "Hi, my name is Alex and I'm from Perlis"
+token = tokenizer(
+    text.split(),
+    is_split_into_words=True,
+    return_tensors="pt"
+).to(model.device)
+with toch.no_grad():
+    output = model(**inputs)
+    prediction = output.logits.argmax(dim=-1)
+    print(prediction)
+```
+## Important Notes & Limitations
+- Chinese text must be tokenized at the character level, not by words
+## Evaluation Result
+- F1 macro: 0.75