DialoGPT-large
Maintainer: microsoft
254
๐๏ธ
| Property | Value |
|---|---|
| Run this model | Run on HuggingFace |
| API spec | View on HuggingFace |
| Github link | No Github link provided |
| Paper link | No paper link provided |
Create account to get full access
Model overview
DialoGPT-large is a state-of-the-art large-scale pretrained dialogue response generation model developed by Microsoft. The human evaluation results indicate that the responses generated by DialoGPT-large are comparable to human response quality in single-turn conversations. The model was trained on 147M multi-turn dialogues from Reddit discussion threads.
Similar models include DialoGPT-small, a smaller version of the model, and the GODEL and GODEL-v1_1-base-seq2seq models, which are large-scale pretrained models for goal-directed dialogues. The PersonaGPT model is also a conversational agent designed to generate personalized responses and incorporate turn-level goals.
Model inputs and outputs
Inputs
- Text: The model takes a sequence of text as input, which represents the conversational context.
Outputs
- Text: The model generates a response text, continuing the conversation based on the input context.
Capabilities
The DialoGPT-large model is capable of engaging in multi-turn conversations, generating responses that are coherent and relevant to the context. The example conversations provided in the model description demonstrate the model's ability to discuss abstract concepts like happiness and wealth, as well as respond appropriately to user prompts.
What can I use it for?
DialoGPT-large can be used to build open-domain conversational agents, chatbots, or dialogue systems. The model's strong performance on single-turn Turing tests suggests it could be a valuable component in interactive applications that require natural and engaging responses. Additionally, the model could be fine-tuned on domain-specific data to create specialized conversational assistants for various use cases.
Things to try
One interesting aspect of DialoGPT-large is its ability to continue a conversation and maintain context over multiple turns. Try providing the model with a longer dialogue history and observe how it builds upon the previous context to generate coherent and relevant responses. You could also experiment with the model's generation parameters, such as temperature and top-k sampling, to explore the diversity and quality of the responses.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
Related Models
๐งช
DialoGPT-medium
297
DialoGPT-medium is a state-of-the-art large-scale pretrained dialogue response generation model developed by Microsoft. It is trained on 147M multi-turn dialogues from Reddit discussion threads, allowing the model to generate human-like responses in open-ended conversations. According to the human evaluation results, the quality of the responses generated by DialoGPT-medium is comparable to human responses in a single-turn conversation Turing test. The DialoGPT-medium model is part of the DialoGPT model family, which also includes the larger DialoGPT-large and the smaller DialoGPT-small versions. These models share the same architecture and training data, but differ in their size and performance characteristics. Model inputs and outputs Inputs Text representing the conversation history between the user and the model Outputs Text representing the model's response to continue the conversation Capabilities DialoGPT-medium is capable of generating coherent and contextually-appropriate multi-turn responses in open-ended conversations. The model can engage in a wide range of conversational topics, from discussing the merits of wealth and happiness to providing empathetic responses. The model's ability to generate human-like responses makes it a useful tool for building conversational AI assistants. What can I use it for? DialoGPT-medium can be used to build conversational AI assistants for a variety of applications, such as customer service, social chatbots, or virtual companions. The model's pretrained nature allows for efficient fine-tuning on specific tasks or domains, making it a versatile tool for building conversational AI systems. Things to try One interesting aspect of DialoGPT-medium is its ability to engage in multi-turn conversations and maintain context over the course of a dialogue. Developers can experiment with using the model to build conversational agents that can remember and reference previous parts of a conversation, allowing for more natural and engaging interactions. Another area to explore is the model's performance on specific conversational tasks or domains, such as task-oriented dialogues or empathetic responses. Developers can fine-tune the model on relevant data to assess its capabilities in these areas.
Updated Invalid Date
๐
DialoGPT-small
86
DialoGPT-small is a state-of-the-art large-scale pretrained dialogue response generation model developed by Microsoft. It is trained on 147M multi-turn dialogues from Reddit discussion threads, allowing it to engage in natural and coherent multi-turn conversations. According to human evaluation results, the quality of responses generated by DialoGPT-small is comparable to human responses in a single-turn conversation Turing test. This model builds on the success of other large language models like GODEL-v1_1-base-seq2seq, personaGPT, and BioGPT, which have shown the potential of large-scale pretraining for various dialogue and language tasks. Model inputs and outputs DialoGPT-small is a text-to-text transformer-based model that takes in a multi-turn dialogue context as input and generates a coherent and relevant response. Inputs Multi-turn dialogue context**: A sequence of messages from a conversation, which the model uses to generate an appropriate next response. Outputs Generated text response**: The model's prediction for the next response in the dialogue, based on the provided context. Capabilities DialoGPT-small has demonstrated strong performance in engaging in natural and coherent multi-turn dialogues. It can understand the context of a conversation and generate relevant, human-like responses. The model is particularly adept at tasks like open-domain chatbots, conversational agents, and dialogue systems where natural language understanding and generation are key. What can I use it for? DialoGPT-small can be used for a variety of applications that require natural language generation and dialogue capabilities, such as: Conversational AI**: Develop chatbots, virtual assistants, and other dialogue systems that can engage in fluid, contextual conversations. Customer service automation**: Automate customer support and help desk tasks by generating relevant responses to user inquiries. Open-domain dialogue**: Create engaging, free-form conversational experiences for entertainment or educational purposes. Language learning**: Provide interactive language practice and feedback for language learners. By fine-tuning DialoGPT-small on domain-specific data, you can adapt it to various industry-specific use cases, such as customer support, e-commerce, healthcare, and more. Things to try One interesting aspect of DialoGPT-small is its ability to maintain coherence and context across multiple turns of a conversation. Try prompting the model with a multi-turn dialogue and see how it responds, keeping the overall flow and tone of the conversation in mind. You can also experiment with providing the model with persona information or specific goals for the dialogue, and observe how it adapts its responses accordingly. Another interesting direction is to explore the model's limitations and biases, as large language models like DialoGPT-small can sometimes generate biased or problematic content. Be mindful of these risks and carefully evaluate the model's outputs, especially for use cases that may impact real people.
Updated Invalid Date
๐งช
GODEL-v1_1-large-seq2seq
85
The GODEL-v1_1-large-seq2seq model is a large-scale pre-trained model for goal-directed dialogs developed by Microsoft. It is parameterized with a Transformer-based encoder-decoder model and trained on 551M multi-turn dialogs from Reddit discussion threads, as well as 5M instruction and knowledge grounded dialogs. This allows the model to effectively generate responses that are grounded in external information beyond just the current conversation context. The GODEL-v1_1-large-seq2seq model is similar to other pre-trained language models like GODEL-v1_1-base-seq2seq, DialoGPT-small, and GPT-2 XL in that they are all large-scale Transformer-based models pre-trained on broad text corpora. However, the GODEL-v1_1-large-seq2seq model is unique in its focus on goal-directed dialog tasks that require conditioning responses on external information. Model inputs and outputs Inputs Instruction**: A description of the task the model should perform, e.g. "given a dialog context, you need to respond empathically." Dialog**: The current conversation context as a sequence of dialog turns. Knowledge**: Any relevant external information to ground the response in, formatted as "[KNOWLEDGE] ". Outputs A generated response to continue the dialog, conditioned on the provided instruction, dialog context, and knowledge. Capabilities The GODEL-v1_1-large-seq2seq model demonstrates strong performance on goal-directed dialog tasks that require incorporating external information beyond just the current conversation. For example, in a chitchat scenario, the model is able to generate empathetic responses based on the provided dialog context. And in a grounded response generation task, the model can leverage the provided knowledge about Stardew Valley mods to continue the conversation in a relevant and informative way. What can I use it for? The GODEL-v1_1-large-seq2seq model could be useful for building conversational AI agents that need to engage in goal-directed dialogs while drawing upon external knowledge. This could include applications like customer service chatbots, virtual assistants, or even creative writing aids. By leveraging the model's pre-training on a large corpus of dialog data, developers can efficiently fine-tune it to accomplish new dialog tasks with just a handful of task-specific examples. Things to try One interesting thing to explore with the GODEL-v1_1-large-seq2seq model is how it handles different types of grounding information. The provided examples show the model using knowledge about Stardew Valley mods, but it would be worth trying other forms of external information as well, such as web pages, books, or even structured data. Seeing how the model's responses vary based on the type and content of the grounding material could yield insights into its capabilities and limitations. Additionally, given the model's focus on goal-directed dialog, it would be interesting to experiment with more complex or open-ended task instructions to see how the model adapts. For example, instead of just asking for an empathetic response, one could provide a more nuanced directive like "generate a response that provides emotional support while also offering practical advice."
Updated Invalid Date
๐
GODEL-v1_1-base-seq2seq
66
GODEL-v1_1-base-seq2seq is a large-scale pre-trained model for goal-directed dialogs developed by Microsoft. It is parameterized with a Transformer-based encoder-decoder model and trained on 551M multi-turn dialogs from Reddit discussion threads, as well as 5M instruction and knowledge-grounded dialogs. The model allows more effective fine-tuning on dialog tasks that require conditioning the response on information that is external to the current conversation, such as a retrieved document. Similar models include GPT-2 XL, a large-scale pre-trained language model, and BioGPT, a domain-specific generative Transformer language model pre-trained on biomedical literature. Model inputs and outputs Inputs Dialog context**: The previous messages in a conversation Instruction**: The task or prompt for the model to respond to Knowledge**: External information relevant to the conversation, such as a retrieved document Outputs Response**: The model's generated response to the provided dialog context, instruction, and knowledge Capabilities The GODEL-v1_1-base-seq2seq model is capable of generating coherent and relevant responses in goal-directed dialog scenarios. It can effectively incorporate external knowledge into its responses, making it useful for tasks that require grounding the conversation in additional information beyond the current context. What can I use it for? The GODEL-v1_1-base-seq2seq model can be used for a variety of dialog-based applications, such as customer service chatbots, virtual assistants, and educational tools. By leveraging its ability to condition responses on external knowledge, the model can be particularly useful for applications that require providing information or recommendations based on a user's query and relevant background data. Things to try One interesting aspect of the GODEL-v1_1-base-seq2seq model is its potential to generate responses that are grounded in external knowledge. You could try providing the model with different types of knowledge, such as product information, scientific papers, or news articles, and see how it incorporates that information into its responses. Additionally, you could experiment with different prompting strategies to see how the model's behavior and output changes based on the specific instruction or task it is given.
Updated Invalid Date