Update README.md

README.md

@@ -6,59 +6,3 @@ metrics:
 - sacrebleu
 pipeline_tag: text2text-generation
 ---
-
-### 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, AutoModelForSeq2SeqLM
-
-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)
-```