add inference code to readme

README.md

@@ -3,5 +3,78 @@ language: ru
 tags:
 - conversational
 ---
+### Description
 
 
+### Inference
+
+```python
+
+def get_length_param(text: str, tokenizer) -> str:
+    tokens_count = len(tokenizer.encode(text))
+    if tokens_count <= 15:
+        len_param = '1'
+    elif tokens_count <= 50:
+        len_param = '2'
+    elif tokens_count <= 256:
+        len_param = '3'
+    else:
+        len_param = '-'
+    return len_param
+
+
+def get_user_param(text: dict, machine_name_in_chat: str) -> str:
+    if text['from'] == machine_name_in_chat:
+        return '1'  # machine
+    else:
+        return '0'  # human
+
+
+chat_history_ids = torch.zeros((1, 0), dtype=torch.int)
+
+while True:
+    
+    next_who = input("Who's phrase?\t")  #input("H / G?")     # Human or GPT
+
+    # In case Human
+    if next_who == "H" or next_who == "Human":
+        input_user = input("===> Human: ")
+        
+        # encode the new user input, add parameters and return a tensor in Pytorch
+        new_user_input_ids = tokenizer.encode(f"|0|{get_length_param(input_user, tokenizer)}|" + input_user + tokenizer.eos_token, return_tensors="pt")
+        # append the new user input tokens to the chat history
+        chat_history_ids = torch.cat([chat_history_ids, new_user_input_ids], dim=-1)
+
+    if next_who == "G" or next_who == "GPT":
+
+        next_len = input("Phrase len? 1/2/3/-\t")  #input("Exp. len?(-/1/2/3): ")
+        # encode the new user input, add parameters and return a tensor in Pytorch
+        new_user_input_ids = tokenizer.encode(f"|1|{next_len}|", return_tensors="pt")
+        # append the new user input tokens to the chat history
+        chat_history_ids = torch.cat([chat_history_ids, new_user_input_ids], dim=-1)
+        
+        # print(tokenizer.decode(chat_history_ids[-1])) # uncomment to see full gpt input
+        
+        # save previous len
+        input_len = chat_history_ids.shape[-1]
+        # generated a response; PS you can read about the parameters at hf.co/blog/how-to-generate
+        chat_history_ids = model.generate(
+            chat_history_ids,
+            num_return_sequences=1,                     # use for more variants, but have to print [i]
+            max_length=512,
+            no_repeat_ngram_size=3,
+            do_sample=True,
+            top_k=50,
+            top_p=0.9,
+            temperature = 0.6,                          # 0 for greedy
+            mask_token_id=tokenizer.mask_token_id,
+            eos_token_id=tokenizer.eos_token_id,
+            unk_token_id=tokenizer.unk_token_id,
+            pad_token_id=tokenizer.pad_token_id,
+            device='cpu'
+        )
+        
+        
+        # pretty print last ouput tokens from bot
+        print(f"===> GPT-3:  {tokenizer.decode(chat_history_ids[:, input_len:][0], skip_special_tokens=True)}")
+```