Update README.md

README.md

@@ -4,4 +4,37 @@ language:
 pipeline_tag: conversational
 tags:
 - pytorch
----
+---
+
+[blenderbot-1B-distill](https://huggingface.co/facebook/blenderbot-1B-distill) fine-tuned on the [ESConv dataset](https://github.com/thu-coai/Emotional-Support-Conversation) and **[AugESC dataset](https://github.com/thu-coai/AugESC)**. Usage example:
+
+```python
+import torch
+from transformers import AutoTokenizer
+from transformers.models.blenderbot import BlenderbotTokenizer, BlenderbotForConditionalGeneration
+
+def _norm(x):
+return ' '.join(x.strip().split())
+
+tokenizer = BlenderbotTokenizer.from_pretrained('thu-coai/blenderbot-400M-esconv')
+model = BlenderbotForConditionalGeneration.from_pretrained('thu-coai/blenderbot-400M-esconv')
+model.eval()
+
+utterances = [
+"I am having a lot of anxiety about quitting my current job. It is too stressful but pays well",
+"What makes your job stressful for you?",
+"I have to deal with many people in hard financial situations and it is upsetting",
+"Do you help your clients to make it to a better financial situation?",
+"I do, but often they are not going to get back to what they want. Many people are going to lose their home when safeguards are lifted",
+]
+input_sequence = ' '.join([' ' + e for e in utterances]) + tokenizer.eos_token # add space prefix and separate utterances with two spaces
+input_ids = tokenizer.convert_tokens_to_ids(tokenizer.tokenize(input_sequence))[-128:]
+input_ids = torch.LongTensor([input_ids])
+
+model_output = model.generate(input_ids, num_beams=1, do_sample=True, top_p=0.9, num_return_sequences=5, return_dict=False)
+generation = tokenizer.batch_decode(model_output, skip_special_tokens=True)
+generation = [_norm(e) for e in generation]
+print(generation)
+
+utterances.append(generation[0]) # for future loop
+```