Pushing model manually

README.md

@@ -1,3 +1,59 @@
 ---
+language: yue
 license: apache-2.0
+tags:
+- generated_from_trainer
+model-index:
+- name: electra-hongkongese-small-hk-ws
+  results: []
 ---
+
+# electra-hongkongese-small-hk-ws
+
+This model is a fine-tuned version of [toastynews/electra-hongkongese-small-discriminator](https://huggingface.co/toastynews/electra-hongkongese-small-discriminator) on [HKCanCor](https://pycantonese.org/data.html#built-in-data) and [CityU](http://sighan.cs.uchicago.edu/bakeoff2005/) for word segmentation.
+
+## Model description
+
+Performs word segmentation on text from Hong Kong.
+There are two versions; hk trained with only text from Hong Kong, and hkt trained with text from Hong Kong and Taiwan. Each version have base and small model sizes.
+
+## Intended uses & limitations
+
+Trained to handle both Hongkongese/Cantonese and Standard Chinese from Hong Kong. Text from other places and English do not work as well. 
+The easiest way is to use with the CKIP Transformers libary. 
+
+## Training and evaluation data
+
+HKCanCor and CityU are converted to BI-encoded word segmentation dataset in Hugging Face format using code from [finetune-ckip-transformers](https://github.com/toastynews/finetune-ckip-transformers).
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 5e-05
+- train_batch_size: 8
+- eval_batch_size: 8
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 3.0
+
+### Training results
+
+|dataset   |token_f |token_p |token_r |
+|:---------|--------|--------|--------|
+|ud yue_hk |  0.9468|  0.9484|  0.9453|
+|ud zh_hk  |  0.9277|  0.9350|  0.9205|
+|_hkcancor_|_0.9769_|_0.9742_|_0.9795_|
+|cityu     |  0.9750|  0.9741|  0.9760|
+|as        |  0.9187|  0.9154|  0.9219|
+
+_Was trained on hkcancor. Reported for reference only._
+
+### Framework versions
+
+- Transformers 4.27.0.dev0
+- Pytorch 1.10.0
+- Datasets 2.10.0
+- Tokenizers 0.13.2