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Model: gorkemgoknar/gpt2-small-turkish
Source: Original Platform

README.md

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+---
+language:
+- tr
+thumbnail:
+tags:
+- gpt2
+- turkish
+
+license: apache-2.0
+datasets:
+- wikipedia-turkish
+metrics:
+- perplexity
+- accuracy
+
+widget:
+- text: Bu yazıyı bir bilgisayar yazdı. Yazarken
+  context: ''
+- text: İnternete kolay erişim sayesinde dünya daha da küçüldü. Bunun sonucunda
+  context: ''
+---
+
+# Turkish GPT2 Model Finetuned 
+# Türkçe GPT2 Modeli
+
+## Model description
+
+This is a GPT2-Small English based model finetuned and additionaly trainied with Wikipedia Articles in Turkish as of 28-10-2020
+
+Live demo based on this work at : https://www.metayazar.com/
+
+Fine tuned writer on this model: https://huggingface.co/gorkemgoknar/gpt2-turkish-writer
+
+Work has been done on Pierre Guillou tutorial as on this page.
+(https://github.com/piegu/fastai-projects/blob/master/finetuning-English-GPT2-any-language-Portuguese-HuggingFace-fastaiv2.ipynb) 
+
+Code is converted to work with Fastai 2.X .
+
+Using Google Colab for training. 
+
+Additional tutorial and source will be in https://github.com/gorkemgoknar in later stage.
+
+Current accuracy 33 %  , Perplexity : 51.88
+
+Models are available:
+
+* [gpt2-small-tuned-tr] (https://huggingface.co/gorkemgoknar/gpt2-small-turkish)
+* [gpt2-small-turkish-writer] (https://huggingface.co/gorkemgoknar/gpt2-turkish-writer)
+
+## Intended uses & limitations
+
+#### How to use
+
+#### Install
+
+```python
+from transformers import AutoTokenizer, AutoModelWithLMHead
+import torch
+
+tokenizer = AutoTokenizer.from_pretrained("gorkemgoknar/gpt2-small-turkish")
+model = AutoModelWithLMHead.from_pretrained("gorkemgoknar/gpt2-small-turkish")
+
+# Get sequence length max of 1024
+tokenizer.model_max_length=1024 
+
+model.eval()  # disable dropout (or leave in train mode to finetune)
+
+```
+
+#### Generate 1 word
+```python
+# input sequence
+text = "Bu yazıyı bilgisayar yazdı."
+inputs = tokenizer(text, return_tensors="pt")
+
+# model output
+outputs = model(**inputs, labels=inputs["input_ids"])
+loss, logits = outputs[:2]
+predicted_index = torch.argmax(logits[0, -1, :]).item()
+predicted_text = tokenizer.decode([predicted_index])
+
+# results
+print('input text:', text)
+print('predicted text:', predicted_text)
+
+# input text: 
+# predicted text:  
+
+```
+
+#### Generate Full Sequence
+```python
+# input sequence
+text = "Bu yazıyı bilgisayar yazdı."
+inputs = tokenizer(text, return_tensors="pt")
+
+# model output using Top-k sampling text generation method
+sample_outputs = model.generate(inputs.input_ids,
+                                pad_token_id=50256,
+                                do_sample=True, 
+                                max_length=50, # put the token number you want
+                                top_k=40,
+                                num_return_sequences=1)
+
+# generated sequence
+for i, sample_output in enumerate(sample_outputs):
+    print(">> Generated text {}\\\\
+\\\\
+{}".format(i+1, tokenizer.decode(sample_output.tolist())))
+
+# >> Generated text
+#    
+
+```
+
+#### Limitations and bias
+
+The training data used for this model come from Turkish Wikipedia. We know it contains a lot of unfiltered content from the internet, which is far from neutral. 
+
+
+## Training data
+
+Wikipedia Turkish article dump as of 28-10-2020
+
+## Training procedure
+
+
+## Eval results
+
+| epoch\\\\t|train_loss\\\\t|valid_loss\\\\t|accuracy\\\\t|perplexity\\\\t|time   |
+| ----- | --------      |---------      | ----------    | ---------     | ----- |
+|0\\\\t|4.777015\\\\t|4.621834\\\\t|0.292547\\\\t|101.680367\\\\t|2:42:05|
+|1\\\\t|4.509412\\\\t|4.403999\\\\t|0.305574\\\\t|81.777267\\\\t|1:09:38|
+|2\\\\t|4.169529\\\\t|4.120755\\\\t|0.324908\\\\t|61.605747\\\\t|1:07:45|
+|3\\\\t|4.293973\\\\t|4.177899\\\\t|0.317211\\\\t|65.228653\\\\t|1:07:02|
+|4\\\\t|4.049848\\\\t|3.949103\\\\t|0.338347\\\\t|51.888783\\\\t|1:05:53|
+
+#Epoch 0 on Tesla T4, others on V100
+
+```
+