Text2Text Generation
Transformers
PyTorch
t5
dialog
text-generation-inference
Inference Endpoints
jianguozhang commited on
Commit
6c5c7e1
·
1 Parent(s): b920714

Update README.md

Browse files
Files changed (1) hide show
  1. README.md +4 -3
README.md CHANGED
@@ -76,9 +76,8 @@ If you already know T5 and Flan-T5, DialogStudio-T5 is better at many things. Wi
76
 
77
  We sample a small amount of dialogues from each commercial supported dataset under three categories of [DialogStudio](https://huggingface.co/datasets/Salesforce/dialogstudio), i.e., KG-Dial, TOD and Open-Domain dialogues. Additionally, we sample at most 150 examples for each non-translation task from [FLAN](https://github.com/google-research/FLAN/tree/main/flan/v2).
78
 
79
- Note:
80
 
81
- Version 1.0 is built on small-scale pre-trained models, this version does not incorporate datasets utilized for training large-scale models (>=7B) like Alpaca, ShareGPT, GPT4ALL, UltraChat from OpenAI's 'GPT-3.5/4', or other datasets such as OASST1 and WizardCoder. As a result, it has certain limitations in terms of writing and creative capabilities. Our initial focus is to update the model versions to enhance existing abilities. Further improvements, including expansion of other capabilities, are part of our roadmap and will be responsive to community requests.
82
 
83
  <img src="https://huggingface.co/datasets/Salesforce/dialogstudio/resolve/main/DialogStudio_Stats.jpg"
84
  alt="drawing" width="700"/>
@@ -240,8 +239,10 @@ The information below in this section are copied and modified from Flan-T5's mod
240
 
241
  We sample a small amount of dialogues from each commercial supported dataset under three categories of [DialogStudio](https://huggingface.co/datasets/Salesforce/dialogstudio), i.e., KG-Dial, TOD and Open-Domain dialogues. Additionally, we sample at most 150 examples for each non-translation task from [FLAN](https://github.com/google-research/FLAN/tree/main/flan/v2).
242
 
243
- Note that this version does not incorporate datasets utilized for training large-scale models (>=7B) like Alpaca, ShareGPT, GPT4ALL, UltraChat from OpenAI's 'GPT-3.5/4', or other datasets such as OASST1 and WizardCoder.
244
 
 
 
 
245
 
246
  See above **Training formats:** for details of the training formats.
247
 
 
76
 
77
  We sample a small amount of dialogues from each commercial supported dataset under three categories of [DialogStudio](https://huggingface.co/datasets/Salesforce/dialogstudio), i.e., KG-Dial, TOD and Open-Domain dialogues. Additionally, we sample at most 150 examples for each non-translation task from [FLAN](https://github.com/google-research/FLAN/tree/main/flan/v2).
78
 
79
+ Note that this model version 1.0 does not incorporate datasets utilized for training large-scale models (>=7B) like Alpaca, ShareGPT, GPT4ALL, UltraChat from OpenAI's 'GPT-3.5/4', or other datasets such as OASST1 and WizardCoder.
80
 
 
81
 
82
  <img src="https://huggingface.co/datasets/Salesforce/dialogstudio/resolve/main/DialogStudio_Stats.jpg"
83
  alt="drawing" width="700"/>
 
239
 
240
  We sample a small amount of dialogues from each commercial supported dataset under three categories of [DialogStudio](https://huggingface.co/datasets/Salesforce/dialogstudio), i.e., KG-Dial, TOD and Open-Domain dialogues. Additionally, we sample at most 150 examples for each non-translation task from [FLAN](https://github.com/google-research/FLAN/tree/main/flan/v2).
241
 
 
242
 
243
+ Note:
244
+
245
+ Model Version 1.0 is built on small-scale pre-trained models, this version does not incorporate datasets utilized for training large-scale models (>=7B) like Alpaca, ShareGPT, GPT4ALL, UltraChat from OpenAI's 'GPT-3.5/4', or other datasets such as OASST1 and WizardCoder. As a result, it has certain limitations in terms of writing and creative capabilities. Our initial focus is to update the model versions to enhance existing abilities. Further improvements, including expansion of other capabilities, are part of our roadmap and will be responsive to community requests.
246
 
247
  See above **Training formats:** for details of the training formats.
248