chatbot_arena_conversations33KEnglishThis dataset contains 33K cleaned conversations with pairwise human preferences. It is collected from 13K unique IP addresses on the Chatbot Arena from April to June 2023. HC337KEnglish, Chinese37,175 instructions generated by ChatGPT and human- ...
A collection of awesome-prompt-datasets, awesome-instruction-dataset, to train ChatLLM such as chatgpt 收录各种各样的指令数据集, 用于训练 ChatLLM 模型。 - jianzhnie/awesome-instruction-datasets
Interestingly, their dataset led to state-of-the-art results even whendialoguesystems were merely pre-trained on it. In future, these findings could lead to the development of more engaging chatbots, which can also be personalized and trained to acquire a particular persona. "We show that tra...
The biggest challenge of building chatbots is training data. The required data must be realistic and large enough to train chatbots. We create a tool to get actual training data from Facebook messenger of a Facebook page. After text preprocessing steps, the newly obtained dataset generates F...
If this parameter is left blank or the value is set to false, datasets are not filtered. Options: true: Filter out only datasets that can be exported. false: Do not filter out only datasets that can be exported. (Default value) train_evaluate_ratio No String Version split ratio for ...
HellaSwag is a challenge dataset for evaluating commonsense NLI that is specially hard for state-of-the-art models, though its questions are trivial for humans (>95% accuracy).
The race to train language models on vast, diverse and inconsistently documented datasets raises pressing legal and ethical concerns. To improve data transparency and understanding, we convene a multi-disciplinary effort between legal and machine learnin
This dataset can be used to train Large Language Models such as GPT, Llama2 and Falcon, both for Fine Tuning and Domain Adaptation.The dataset has the following specs:Use Case: Intent Detection Vertical: Customer Service 27 intents assigned to 10 categories 26872 question/answer pairs, around ...
Use the default format if you plan to train a custom model or if you are writing a custom adapter. This is the most flexible format because you can annotateSlotsandIntentswith custom entity arguments, and they all will be present at the generated output, so for example, you could also in...
The application of machine learning (ML) algorithms to address problems related to model-driven engineering (MDE) is currently hindered by the lack of cura