Added huseinzol05/gpt2-345M-bahasa-cased (#4102)

model_cards/huseinzol05/gpt2-345M-bahasa-cased/README.md

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+---
+language: malay
+---
+
+# Bahasa GPT2 Model
+
+Pretrained GPT2 345M model for Malay.
+
+## Pretraining Corpus
+
+`gpt2-345M-bahasa-cased` model was pretrained on ~0.9 Billion words. We trained on standard language structure only, and below is list of data we trained on,
+
+1. [dumping wikipedia](https://github.com/huseinzol05/Malaya-Dataset#wikipedia-1).
+2. [local news](https://github.com/huseinzol05/Malaya-Dataset#public-news).
+3. [local parliament text](https://github.com/huseinzol05/Malaya-Dataset#parliament).
+4. [local singlish/manglish text](https://github.com/huseinzol05/Malaya-Dataset#singlish-text).
+5. [IIUM Confession](https://github.com/huseinzol05/Malaya-Dataset#iium-confession).
+6. [Wattpad](https://github.com/huseinzol05/Malaya-Dataset#wattpad).
+7. [Academia PDF](https://github.com/huseinzol05/Malaya-Dataset#academia-pdf).
+8. [Common-Crawl](https://github.com/huseinzol05/malaya-dataset#common-crawl).
+
+Preprocessing steps can reproduce from here, [Malaya/pretrained-model/preprocess](https://github.com/huseinzol05/Malaya/tree/master/pretrained-model/preprocess).
+
+## Pretraining details
+
+- This model was trained using GPT2's github [repository](https://github.com/openai/gpt-2) on a V3-8 TPU.
+- All steps can reproduce from here, [Malaya/pretrained-model/gpt2](https://github.com/huseinzol05/Malaya/tree/master/pretrained-model/gpt2).
+
+## Load Pretrained Model
+
+You can use this model by installing `torch` or `tensorflow` and Huggingface library `transformers`. And you can use it directly by initializing it like this:  
+
+```python
+from transformers import GPT2Tokenizer, GPT2Model
+
+model = GPT2Model.from_pretrained('huseinzol05/gpt2-345M-bahasa-cased')
+tokenizer = GPT2Tokenizer.from_pretrained(
+    'huseinzol05/gpt2-345M-bahasa-cased',
+)
+```
+
+## Example using GPT2LMHeadModel
+
+```python
+from transformers import GPT2Tokenizer, GPT2LMHeadModel
+
+tokenizer = GPT2Tokenizer.from_pretrained('huseinzol05/gpt2-345M-bahasa-cased')
+model = GPT2LMHeadModel.from_pretrained(
+    'huseinzol05/gpt2-345M-bahasa-cased', pad_token_id = tokenizer.eos_token_id
+)
+
+input_ids = tokenizer.encode(
+    'penat bak hang, macam ni aku takmau kerja dah', return_tensors = 'pt'
+)
+sample_outputs = model.generate(
+    input_ids,
+    do_sample = True,
+    max_length = 50,
+    top_k = 50,
+    top_p = 0.95,
+    num_return_sequences = 3,
+)
+
+print('Output:\n' + 100 * '-')
+for i, sample_output in enumerate(sample_outputs):
+    print(
+        '{}: {}'.format(
+            i, tokenizer.decode(sample_output, skip_special_tokens = True)
+        )
+    )
+```
+
+Output is,
+
+```text
+Output:
+----------------------------------------------------------------------------------------------------
+0: penat bak hang, macam ni aku takmau kerja dah dekat 2,3 jam.
+Aku harap aku dapat berjimat banyak.
+Ini pun masa kerja, bila dah kerja jadi satu.
+Aku buat kerja ni la.
+Aku memang kalau ada
+1: penat bak hang, macam ni aku takmau kerja dah.
+Tapi nak buat macam mana kan, aku tolong bentang tugas.
+Dan, memang sangat-sangat tak mahu buat kerja sekarang ni.
+Aku pun suka sangat kerja di luar bandar
+2: penat bak hang, macam ni aku takmau kerja dah pun.
+Takpa nak buat kerja-kerja sampingan, baru boleh dapat hadiah pulak.
+Ni la tempat paling best bila duduk di restoran yang ada pekena kopi.
+Cumanya
+```