hon9kon9ize
/

bert-large-cantonese

@@ -1,199 +1,82 @@
 ---
 library_name: transformers
-tags: []
 ---
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 ---
 library_name: transformers
+language:
+  - yue
+license: cc-by-4.0
+tags:
+  - generated_from_trainer
+pipeline_tag: fill-mask
+widget:
+  - text: 香港原本[MASK]一個人煙稀少嘅漁港。
+    example_title: 係
+model-index:
+  - name: bert-large-cantonese
+    results: []
 ---
+# bert-large-cantonese
+## Description
+This model is tranied from scratch on Cantonese text. It is a BERT model with a large architecture (24-layer, 1024-hidden, 16-heads, 326M parameters).
+The first training stage is to pre-train the model on 128 length sequences with a batch size of 512 for 1 epoch. the second stage is to continued pre-train the model on 512 length sequences with a batch size of 512 for one more epoch.
+## How to use
+You can use this model directly with a pipeline for masked language modeling:
+```python
+from transformers import pipeline
+mask_filler = pipeline(
+    "fill-mask",
+    model="hon9kon9ize/bert-large-cantonese
+)
+mask_filler("雞蛋六隻，糖呢就兩茶匙，仲有[MASK]橙皮添。")
+; [{'score': 0.08160534501075745,
+;   'token': 943,
+;   'token_str': '個',
+;   'sequence': '雞 蛋 六 隻 ， 糖 呢 就 兩 茶 匙 ， 仲 有 個 橙 皮 添 。'},
+;  {'score': 0.06182105466723442,
+;   'token': 1576,
+;   'token_str': '啲',
+;   'sequence': '雞 蛋 六 隻 ， 糖 呢 就 兩 茶 匙 ， 仲 有 啲 橙 皮 添 。'},
+;  {'score': 0.04600336775183678,
+;   'token': 1646,
+;   'token_str': '嘅',
+;   'sequence': '雞 蛋 六 隻 ， 糖 呢 就 兩 茶 匙 ， 仲 有 嘅 橙 皮 添 。'},
+;  {'score': 0.03743772581219673,
+;   'token': 3581,
+;   'token_str': '橙',
+;   'sequence': '雞 蛋 六 隻 ， 糖 呢 就 兩 茶 匙 ， 仲 有 橙 橙 皮 添 。'},
+;  {'score': 0.031560592353343964,
+;   'token': 5148,
+;   'token_str': '紅',
+;   'sequence': '雞 蛋 六 隻 ， 糖 呢 就 兩 茶 匙 ， 仲 有 紅 橙 皮 添 。'}]
+```
+## Training hyperparameters
+The following hyperparameters were used during first training:
+- Batch size: 512
+- Learning rate: 1e-4
+- Learning rate scheduler: linear decay
+- 1 Epoch
+- Warmup ratio: 0.1
+Loss plot on [WanDB](https://api.wandb.ai/links/indiejoseph/v3ljlpmp)
+The following hyperparameters were used during second training:
+- Batch size: 512
+- Learning rate: 5e-5
+- Learning rate scheduler: linear decay
+- 1 Epoch
+- Warmup ratio: 0.1
+Loss plot on [WanDB](https://api.wandb.ai/links/indiejoseph/vcm3q1ef)