mesolitica
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embedding-malaysian-mistral-64M-32k

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 ---
+language:
+- ms
 ---
+# 64M 32768 context length Malaysian Mistral Embedding
+768 embedding size, trained on truncated 20k context length, but infer able to scale up to 32k context length.
+README at https://github.com/mesolitica/llm-embedding/tree/main/mistral-embedding
+WandB, https://wandb.ai/huseinzol05/embedding-malaysian-mistral-64M-32768
+## how-to
+```python
+from transformers import AutoModel, AutoTokenizer
+from sklearn.metrics.pairwise import cosine_similarity
+model = AutoModel.from_pretrained('mesolitica/embedding-malaysian-mistral-64M-32k', trust_remote_code = True)
+tokenizer = AutoTokenizer.from_pretrained('mesolitica/embedding-malaysian-mistral-64M-32k')
+input_ids = tokenizer(
+    [
+        'tak suka ayam',
+        'Isu perkauman: Kerajaan didakwa terdesak kaitkan pemimpin PN',
+        'nasi ayam tu sedap',
+        'suka ikan goreng?',
+        'Kerajaan tidak akan berkompromi dengan isu perkauman dan agama yang dimanipulasi pihak tertentu untuk mengganggu-gugat kestabilan negara serta ketenteraman rakyat.',
+        'rasis bodo mamat tu',
+        'kerajaan sekarang xde otak',
+        'aku nak sukan olimpik ni',
+        'malaysia dapat x pingat kt sukan asia?',
+        'pingat gangsa menerusi terjun dan olahraga pada hari ke-10',
+        'Kerajaan negeri kini dibenarkan melaksanakan penerokaan awal unsur nadir bumi (REE) berdasarkan prosedur operasi standard (SOP) sedia ada untuk perlombongan nadir bumi dan mineral.',
+        'KONTINJEN Malaysia mendekati sasaran 27 pingat di Sukan Asia kali ini esok, selepas menuai dua lagi pingat gangsa menerusi terjun dan olahraga pada hari ke-10 pertandingan, pada Selasa.'
+    ],
+    return_tensors = 'pt',
+    padding = True
+)
+v = model.encode(input_ids).detach().numpy()
+v.shape
+```
+```
+(12, 768)
+```