fix readme

README.md

@@ -2,13 +2,13 @@
 language: ms
 ---
 
-# t5-small-bahasa-cased
+# t5-tiny-bahasa-cased
 
-Pretrained T5 small language model for Malay. 
+Pretrained T5 tiny language model for Malay. 
 
 ## Pretraining Corpus
 
-`t5-small-bahasa-cased` model was pretrained on multiple tasks. Below is list of tasks we trained on,
+`t5-tiny-bahasa-cased` model was pretrained on multiple tasks. Below is list of tasks we trained on,
 
 1. Language masking task on bahasa news, bahasa Wikipedia, bahasa Academia.edu, bahasa parliament and translated The Pile.
 2. News title prediction on bahasa news.
@@ -35,8 +35,8 @@ You can use this model by installing `torch` or `tensorflow` and Huggingface lib
 ```python
 from transformers import T5Tokenizer, T5Model
 
-model = T5Model.from_pretrained('malay-huggingface/t5-small-bahasa-cased')
-tokenizer = T5Tokenizer.from_pretrained('malay-huggingface/t5-small-bahasa-cased')
+model = T5Model.from_pretrained('malay-huggingface/t5-tiny-bahasa-cased')
+tokenizer = T5Tokenizer.from_pretrained('malay-huggingface/t5-tiny-bahasa-cased')
 ```
 
 ## Example using T5ForConditionalGeneration
@@ -44,8 +44,8 @@ tokenizer = T5Tokenizer.from_pretrained('malay-huggingface/t5-small-bahasa-cased
 ```python
 from transformers import T5Tokenizer, T5ForConditionalGeneration
 
-tokenizer = T5Tokenizer.from_pretrained('malay-huggingface/t5-small-bahasa-cased')
-model = T5ForConditionalGeneration.from_pretrained('malay-huggingface/t5-small-bahasa-cased')
+tokenizer = T5Tokenizer.from_pretrained('malay-huggingface/t5-tiny-bahasa-cased')
+model = T5ForConditionalGeneration.from_pretrained('malay-huggingface/t5-tiny-bahasa-cased')
 input_ids = tokenizer.encode('soalan: siapakah perdana menteri malaysia?', return_tensors = 'pt')
 outputs = model.generate(input_ids)
 print(tokenizer.decode(outputs[0]))

convert-from-malaya.ipynb

@@ -596,13 +596,6 @@
    "source": [
     "!rm -rf t5-tiny-v2"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {