
## DialoGPT_MWOZ_Idioms

This is a fine-tuned model of DialoGPT (medium)-MultiWOZ on the PIE-English idioms corpus. It is intended to be used as an idiom-aware conversational system.
The dataset it's trained on is limited in scope, as it covers only 10 classes of idioms ( metaphor, simile, euphemism, parallelism, personification, oxymoron, paradox, hyperbole, irony and literal).

The perplexity achieved on the test and validation sets are 160.9 and 160.46, respectively. More information about the original pre-trained model can be found [here](https://huggingface.co/tosin/dialogpt_mwoz)

* Generation example from an interactive environment:

|Role | Response |
|---------|------------|
|User | remind her to take it off the hook. |
|Bot | the bookie took it on the nod |
|User | A lengthy break is in order ; so make sure you keep your nose clean |
|Bot | they are under a cloud because of fears about US President's wife Hillary Clinton |
|User | Some families breed like rabbits and send us all their male heirs |
|Bot | he was out for the count |

Please find the information about preprocessing, training and full details of the DialoGPT in the [original DialoGPT repository](https://github.com/microsoft/DialoGPT)

### 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("tosin/dialogpt_mwoz_idioms")
model = AutoModelForCausalLM.from_pretrained("tosin/dialogpt_mwoz_idioms")
# Let's chat for 5 lines
for step in range(5):
	# encode the new user input, add the eos_token and return a tensor in Pytorch
	new_user_input_ids = tokenizer.encode(input(">> User:") + 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, 
	chat_history_ids = model.generate(bot_input_ids, max_length=1000, pad_token_id=tokenizer.eos_token_id)
	# pretty print last ouput tokens from bot
	print("DialoGPT_MWOZ_Bot: {}".format(tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)))


{"language": ["en"], "license": "cc-by-4.0", "tags": ["conversational", "transformers"], "datasets": ["multi_woz_v22 and PIE-English idioms corpus"], "metrics": ["perplexity"], "thumbnail": "https://huggingface.co/front/thumbnails/dialogpt.png", "widget": [{"text": "Does that mean Jane is off the hook?"}]}
transformers pytorch gpt2 text-generation conversational en license:cc-by-4.0 autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #en #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

DialoGPT\_MWOZ\_Idioms
----------------------


This is a fine-tuned model of DialoGPT (medium)-MultiWOZ on the PIE-English idioms corpus. It is intended to be used as an idiom-aware conversational system.
The dataset it's trained on is limited in scope, as it covers only 10 classes of idioms ( metaphor, simile, euphemism, parallelism, personification, oxymoron, paradox, hyperbole, irony and literal).


The perplexity achieved on the test and validation sets are 160.9 and 160.46, respectively. More information about the original pre-trained model can be found here


* Generation example from an interactive environment:



Please find the information about preprocessing, training and full details of the DialoGPT in the original DialoGPT repository


### 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("tosin/dialogpt\_mwoz\_idioms")
model = AutoModelForCausalLM.from\_pretrained("tosin/dialogpt\_mwoz\_idioms")


Let's chat for 5 lines
======================


for step in range(5):
# encode the new user input, add the eos\_token and return a tensor in Pytorch
new\_user\_input\_ids = URL(input(">> User:") + 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,
chat\_history\_ids = model.generate(bot\_input\_ids, max\_length=1000, pad\_token\_id=tokenizer.eos\_token\_id)
# pretty print last ouput tokens from bot
print("DialoGPT\_MWOZ\_Bot: {}".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("tosin/dialogpt\_mwoz\_idioms")
model = AutoModelForCausalLM.from\_pretrained("tosin/dialogpt\_mwoz\_idioms")


Let's chat for 5 lines
======================


for step in range(5): # encode the new user input, add the eos\_token and return a tensor in Pytorch
new\_user\_input\_ids = URL(input(">> User:") + 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,
chat\_history\_ids = model.generate(bot\_input\_ids, max\_length=1000, pad\_token\_id=tokenizer.eos\_token\_id) # pretty print last ouput tokens from bot
print("DialoGPT\_MWOZ\_Bot: {}".format(URL(chat\_history\_ids[:, bot\_input\_ids.shape[-1]:][0], skip\_special\_tokens=True)))
TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #en #license-cc-by-4.0 #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("tosin/dialogpt\_mwoz\_idioms")
model = AutoModelForCausalLM.from\_pretrained("tosin/dialogpt\_mwoz\_idioms")


Let's chat for 5 lines
======================


for step in range(5): # encode the new user input, add the eos\_token and return a tensor in Pytorch
new\_user\_input\_ids = URL(input(">> User:") + 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,
chat\_history\_ids = model.generate(bot\_input\_ids, max\_length=1000, pad\_token\_id=tokenizer.eos\_token\_id) # pretty print last ouput tokens from bot
print("DialoGPT\_MWOZ\_Bot: {}".format(URL(chat\_history\_ids[:, bot\_input\_ids.shape[-1]:][0], skip\_special\_tokens=True)))
passage: TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #en #license-cc-by-4.0 #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("tosin/dialogpt\_mwoz\_idioms")
model = AutoModelForCausalLM.from\_pretrained("tosin/dialogpt\_mwoz\_idioms")


Let's chat for 5 lines
======================


for step in range(5):# encode the new user input, add the eos\_token and return a tensor in Pytorch
new\_user\_input\_ids = URL(input(">> User:") + 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,
chat\_history\_ids = model.generate(bot\_input\_ids, max\_length=1000, pad\_token\_id=tokenizer.eos\_token\_id)# pretty print last ouput tokens from bot
print("DialoGPT\_MWOZ\_Bot: {}".format(URL(chat\_history\_ids[:, bot\_input\_ids.shape[-1]:][0], skip\_special\_tokens=True)))