README added

README.md

@@ -0,0 +1,48 @@
+---
+thumbnail: https://huggingface.co/front/thumbnails/dialogpt.png
+language:
+- en
+license: cc-by-4.0
+tags:
+- conversational
+- transformers
+datasets:
+- multi_woz_v22 and PIE-English idioms corpus
+metrics:
+- perplexity
+widget:
+- text: "Does that mean Jane is off the hook?"
+---
+
+## 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)))
+