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+thumbnail: https://huggingface.co/front/thumbnails/dialogpt.png
+tags:
+- conversational
 license: mit
 ---
+
+### Large-Scale Pre-Training for Goal-Directed Dialog (GODEL)
+
+GODEL is a large-scale pre-trained model for goal-directed dialogs. It is parameterized with a Transformer-based encoder-decoder model and trained for response generation grounded in external text, which allows more effective fine-tuning on dialog tasks that require conditioning the response on information that is external to the current conversation (e.g., a retrieved document). The pre-trained model can be efficiently fine-tuned and adapted to accomplish a new dialog task with a handful of task-specific dialogs. The v1.1 model is trained on 551M multi-turn dialogs from Reddit discussion thread, and 5M instruction and knowledge grounded dialogs.
+
+##### Multi-turn generation examples from an interactive environment:
+Chitchat example:
+> Instruction: given a dialog context, you need to response empathically.  <br>
+> User: Does money buy happiness? <br>
+> Agent: It is a question. Money buys you a lot of things, but not enough to buy happiness. <br>
+> User: What is the best way to buy happiness ? <br>
+> Agent: Happiness is bought through your experience and not money. <br>
+
+Grounded response generation example:
+> Instruction: given a dialog context and related knowledge, you need to response safely based on the knowledge. <br>
+> Knowledge: The best Stardew Valley mods PCGamesN_0 / About SMAPI <br>
+> User: My favorite game is stardew valley. stardew valley is very fun. <br>
+> Agent: I love Stardew Valley mods, like PCGamesN_0 / About SMAPI. <br>
+
+Please find the information about preprocessing, training and full details of the GODEL in the [project webpage](https://aka.ms/GODEL).
+
+ArXiv paper: [https://arxiv.org/abs/2206.11309](https://arxiv.org/abs/2206.11309)
+
+### How to use
+
+Now we are ready to try out how the model works as a chatting partner!
+
+```python
+
+from transformers import AutoTokenizer,AutoModel
+
+tokenizer = AutoTokenizer.from_pretrained("microsoft/GODEL-v1_1-base-seq2seq")
+model = AutoModelForSeq2SeqLM.from_pretrained("microsoft/GODEL-v1_1-base-seq2seq")
+
+def generate(instruction, knowledge, dialog):
+    if knowledge != '':
+        knowledge = '[KNOWLEDGE] ' + knowledge
+    dialog = ' EOS '.join(dialog)
+    query = f"{instruction} [CONTEXT] {dialog} {knowledge}"
+    input_ids = tokenizer(f"{query}", return_tensors="pt").input_ids
+    outputs = model.generate(input_ids, max_length=128, min_length=8, top_p=0.9, do_sample=True)
+    output = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return output
+
+# Instruction for a chitchat task
+instruction = f'Instruction: given a dialog context, you need to response empathically.'
+# Leave the knowldge empty
+knowledge = ''
+dialog = [
+    'Does money buy happiness?',
+    'It is a question. Money buys you a lot of things, but not enough to buy happiness.',
+    'What is the best way to buy happiness ?'
+]
+response = generate(instruction, knowledge, dialog)
+print(response)
+```
+
+### Citation
+if you use this code and data in your research, please cite our arxiv paper:
+```
+@misc{peng2022godel,
+author = {Peng, Baolin and Galley, Michel and He, Pengcheng and Brockett, Chris and Liden, Lars and Nouri, Elnaz and Yu, Zhou and Dolan, Bill and Gao, Jianfeng},
+title = {GODEL: Large-Scale Pre-training for Goal-Directed Dialog},
+howpublished = {arXiv},
+year = {2022},
+month = {June},
+url = {https://www.microsoft.com/en-us/research/publication/godel-large-scale-pre-training-for-goal-directed-dialog/},
+}
+```
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