
[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). 

See the [original paper](https://arxiv.org/abs/2202.13047) for details. 

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-1B-augesc')
model = BlenderbotForConditionalGeneration.from_pretrained('thu-coai/blenderbot-1B-augesc')
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
```


Please kindly cite our papers if you use this model:

```bib
@inproceedings{liu-etal-2021-towards,
  title={Towards Emotional Support Dialog Systems},
  author={Liu, Siyang  and 
    Zheng, Chujie  and 
    Demasi, Orianna  and 
    Sabour, Sahand  and 
    Li, Yu  and 
    Yu, Zhou  and 
    Jiang, Yong  and 
    Huang, Minlie},
  booktitle={ACL},
  year={2021}
}

@inproceedings{zheng-etal-2023-augesc,
  title={AugESC: Dialogue Augmentation with Large Language Models for Emotional Support Conversation},
  author={Zheng, Chujie and
    Sabour, Sahand and
    Wen, Jiaxin and
    Zhang, Zheng and
    Huang, Minlie},
  booktitle={Findings of ACL},
  year={2023}
}
```
{"language": ["en"], "license": "cc-by-nc-4.0", "tags": ["pytorch"], "pipeline_tag": "conversational"}
transformers pytorch safetensors blenderbot text2text-generation conversational en arxiv:2202.13047 license:cc-by-nc-4.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #safetensors #blenderbot #text2text-generation #conversational #en #arxiv-2202.13047 #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #region-us 


blenderbot-1B-distill fine-tuned on the ESConv dataset and AugESC dataset. 

See the original paper for details. 

Usage example:




Please kindly cite our papers if you use this model:


TAGS
#transformers #pytorch #safetensors #blenderbot #text2text-generation #conversational #en #arxiv-2202.13047 #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #region-us 

TAGS
#transformers #pytorch #safetensors #blenderbot #text2text-generation #conversational #en #arxiv-2202.13047 #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #region-us 
