### How to use

Now we are ready to try out how the model works as a chatting partner!

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch


tokenizer = AutoTokenizer.from_pretrained("keonju/chat_bot")
model = AutoModelForCausalLM.from_pretrained("keonju/chat_bot")

# Let's chat for 5 lines
for step in range(5):
	 message = input("MESSAGE: ")

        if message in ["", "q"]:  # if the user doesn't wanna talk
            break

        # encode the new user input, add the eos_token and return a tensor in Pytorch
        new_user_input_ids = tokenizer.encode(message + tokenizer.eos_token, return_tensors='pt')

        # append the new user input tokens to the chat history
        bot_input_ids = torch.cat([chat_history_ids, new_user_input_ids], dim=-1) if step > 0 else new_user_input_ids
        
       
        # generated a response while limiting the total chat history to 1000 tokens, 
        if (trained):
            chat_history_ids = model.generate(
                bot_input_ids, 
                max_length=1000,
                pad_token_id=tokenizer.eos_token_id,  
                no_repeat_ngram_size=3,       
                do_sample=True, 
                top_k=100, 
                top_p=0.7,
                temperature = 0.8, 
            )
        else:
            chat_history_ids = model.generate(
                bot_input_ids, 
                max_length=1000, 
                pad_token_id=tokenizer.eos_token_id,
                no_repeat_ngram_size=3
            )

        # pretty print last ouput tokens from bot
        print("DialoGPT: {}".format(tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)))

{"language": ["ko"], "tags": ["conversational"], "pipeline_tag": "conversational"}
transformers pytorch gpt2 text-generation conversational ko autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #ko #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

### How to use

Now we are ready to try out how the model works as a chatting partner!

'''python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch


tokenizer = AutoTokenizer.from_pretrained("keonju/chat_bot")
model = AutoModelForCausalLM.from_pretrained("keonju/chat_bot")

# Let's chat for 5 lines
for step in range(5):
	 message = input("MESSAGE: ")

        if message in ["", "q"]:  # if the user doesn't wanna talk
            break

        # encode the new user input, add the eos_token and return a tensor in Pytorch
        new_user_input_ids = URL(message + tokenizer.eos_token, return_tensors='pt')

        # append the new user input tokens to the chat history
        bot_input_ids = URL([chat_history_ids, new_user_input_ids], dim=-1) if step > 0 else new_user_input_ids
        
       
        # generated a response while limiting the total chat history to 1000 tokens, 
        if (trained):
            chat_history_ids = model.generate(
                bot_input_ids, 
                max_length=1000,
                pad_token_id=tokenizer.eos_token_id,  
                no_repeat_ngram_size=3,       
                do_sample=True, 
                top_k=100, 
                top_p=0.7,
                temperature = 0.8, 
            )
        else:
            chat_history_ids = model.generate(
                bot_input_ids, 
                max_length=1000, 
                pad_token_id=tokenizer.eos_token_id,
                no_repeat_ngram_size=3
            )

        # pretty print last ouput tokens from bot
        print("DialoGPT: {}".format(URL(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)))

### How to use

Now we are ready to try out how the model works as a chatting partner!

'''python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch


tokenizer = AutoTokenizer.from_pretrained("keonju/chat_bot")
model = AutoModelForCausalLM.from_pretrained("keonju/chat_bot") # Let's chat for 5 lines
for step in range(5):
	 message = input("MESSAGE: ")

        if message in ["", "q"]:  # if the user doesn't wanna talk
            break

        # encode the new user input, add the eos_token and return a tensor in Pytorch
        new_user_input_ids = URL(message + tokenizer.eos_token, return_tensors='pt')

        # append the new user input tokens to the chat history
        bot_input_ids = URL([chat_history_ids, new_user_input_ids], dim=-1) if step > 0 else new_user_input_ids
        
       
        # generated a response while limiting the total chat history to 1000 tokens, 
        if (trained):
            chat_history_ids = model.generate(
                bot_input_ids, 
                max_length=1000,
                pad_token_id=tokenizer.eos_token_id,  
                no_repeat_ngram_size=3,       
                do_sample=True, 
                top_k=100, 
                top_p=0.7,
                temperature = 0.8, 
            )
        else:
            chat_history_ids = model.generate(
                bot_input_ids, 
                max_length=1000, 
                pad_token_id=tokenizer.eos_token_id,
                no_repeat_ngram_size=3
            )

        # pretty print last ouput tokens from bot
        print("DialoGPT: {}".format(URL(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)))
TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #ko #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
 ### How to use

Now we are ready to try out how the model works as a chatting partner!

'''python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch


tokenizer = AutoTokenizer.from_pretrained("keonju/chat_bot")
model = AutoModelForCausalLM.from_pretrained("keonju/chat_bot") # Let's chat for 5 lines
for step in range(5):
	 message = input("MESSAGE: ")

        if message in ["", "q"]:  # if the user doesn't wanna talk
            break

        # encode the new user input, add the eos_token and return a tensor in Pytorch
        new_user_input_ids = URL(message + tokenizer.eos_token, return_tensors='pt')

        # append the new user input tokens to the chat history
        bot_input_ids = URL([chat_history_ids, new_user_input_ids], dim=-1) if step > 0 else new_user_input_ids
        
       
        # generated a response while limiting the total chat history to 1000 tokens, 
        if (trained):
            chat_history_ids = model.generate(
                bot_input_ids, 
                max_length=1000,
                pad_token_id=tokenizer.eos_token_id,  
                no_repeat_ngram_size=3,       
                do_sample=True, 
                top_k=100, 
                top_p=0.7,
                temperature = 0.8, 
            )
        else:
            chat_history_ids = model.generate(
                bot_input_ids, 
                max_length=1000, 
                pad_token_id=tokenizer.eos_token_id,
                no_repeat_ngram_size=3
            )

        # pretty print last ouput tokens from bot
        print("DialoGPT: {}".format(URL(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)))
TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #ko #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
### How to use

Now we are ready to try out how the model works as a chatting partner!

'''python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch


tokenizer = AutoTokenizer.from_pretrained("keonju/chat_bot")
model = AutoModelForCausalLM.from_pretrained("keonju/chat_bot")# Let's chat for 5 lines
for step in range(5):
	 message = input("MESSAGE: ")

        if message in ["", "q"]:  # if the user doesn't wanna talk
            break

        # encode the new user input, add the eos_token and return a tensor in Pytorch
        new_user_input_ids = URL(message + tokenizer.eos_token, return_tensors='pt')

        # append the new user input tokens to the chat history
        bot_input_ids = URL([chat_history_ids, new_user_input_ids], dim=-1) if step > 0 else new_user_input_ids
        
       
        # generated a response while limiting the total chat history to 1000 tokens, 
        if (trained):
            chat_history_ids = model.generate(
                bot_input_ids, 
                max_length=1000,
                pad_token_id=tokenizer.eos_token_id,  
                no_repeat_ngram_size=3,       
                do_sample=True, 
                top_k=100, 
                top_p=0.7,
                temperature = 0.8, 
            )
        else:
            chat_history_ids = model.generate(
                bot_input_ids, 
                max_length=1000, 
                pad_token_id=tokenizer.eos_token_id,
                no_repeat_ngram_size=3
            )

        # pretty print last ouput tokens from bot
        print("DialoGPT: {}".format(URL(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)))