Update README.md

README.md

@@ -2,4 +2,50 @@
 license: apache-2.0
 ---
 ## Model description
-GPT-2 model
+GPT-2 model from Lithuania using Wikipedia corpus dataset based on GPT-2 small model.
+
+This is only the first version of the model, over time model will be improved using a bigger dataset and better data preparation.
+
+## Training data
+This model was pre-trained with 180MB of Lithuanian Wikipedia. The texts are tokenized using a byte-level version of Byte Pair Encoding (BPE).
+
+## Training
+The model was trained on wiki-corpus for 40 hours using NVIDIA Tesla P100 GPU.
+
+##How to use
+
+### Load model
+
+``` from transformers import AutoTokenizer, TFAutoModelWithLMHead
+import tensorflow as tf
+
+tokenizer = AutoTokenizer.from_pretrained("DeividasM/gpt2_lithuanian_small")
+model = TFAutoModelWithLMHead.from_pretrained("DeividasM/gpt2_lithuanian_small")
+
+# Get sequence length max of 1024
+tokenizer.model_max_length=1024 
+
+model.eval()
+```
+### Generate text
+
+``` text = "tekstas"
+inputs = tokenizer.encode(text, return_tensors="tf")
+
+
+outputs = model.generate(inputs, eos_token_id=50256, pad_token_id=50256, 
+                         do_sample=True,
+                         max_length=40,
+                         top_k=40)
+                         
+print(tokenizer.decode(outputs[0]))
+
+```
+### Limitations and bias
+The training data used for this model come from Lithuanian Wikipedia. We know it contains a lot of unfiltered content from the internet, which is far from neutral. As the openAI team themselves point out in their model card:
+
+"Because large-scale language models like GPT-2 do not distinguish fact from fiction, we don’t support use-cases that require the generated text to be true. Additionally, language models like GPT-2 reflect the biases inherent to the systems they were trained on, so we do not recommend that they be deployed into systems that interact with humans > unless the deployers first carry out a study of biases relevant to the intended use-case. We found no statistically significant difference in gender, race, and religious bias probes between 774M and 1.5B, implying all versions of GPT-2 should be approached with similar levels of caution around use cases that are sensitive to biases around human attributes."
+
+### Author
+
+Lithuanian GPT-2 small was trained and evaluated by Deividas Mataciunas