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Welcome to our conversational AI model, designed to engage users in natural and meaningful conversations across various topics. Our AI, powered by state-of-the-art natural language processing techniques, is adept at understanding and generating human-like responses, making interactions with it seamless and enjoyable.

Key Features:

Natural Conversations: Our AI is trained on vast amounts of conversational data, enabling it to understand context, tone, and nuances in language to deliver engaging and contextually relevant responses.

Multi-Topic Expertise: Whether you want to discuss technology, science, arts, or everyday topics, our AI is equipped to converse on a wide range of subjects, providing informative and entertaining responses.

Personalization: Our AI can adapt its responses based on user input and preferences, creating a personalized conversational experience for each user.

Continual Learning: Constantly evolving and learning from user interactions, our AI continually improves its conversational abilities, ensuring that it stays up-to-date and relevant.

Use Cases:

Customer Support: Enhance customer support experiences by providing instant responses to customer queries and issues.
Virtual Assistants: Create virtual assistants for websites or applications to help users with tasks, information retrieval, and recommendations.
Education: Facilitate interactive learning experiences by engaging students in educational conversations and providing explanations on various topics.
Entertainment: Offer entertaining and engaging interactions for users seeking casual conversation or companionship.
Ethical Considerations:

We prioritize ethical considerations in the development and deployment of our conversational AI model. We are committed to ensuring user privacy, maintaining transparency about the AI's capabilities, and mitigating biases in its responses.

Get Started:

Experience the power of our conversational AI model today and engage in captivating conversations on a multitude of topics. Start a conversation and discover the endless possibilities of AI-driven interactions!

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  library_name: fairseq
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+ pipeline_tag: text2text-generation
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+ ---
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+ # Model Card for Model ID
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+
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+ <!-- Provide a quick summary of what the model is/does. -->
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+ This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
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+ ## Model Details
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+ ### Model Description
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+ <!-- Provide a longer summary of what this model is. -->
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+ - **Developed by:** [More Information Needed]
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+ - **Funded by [optional]:** [More Information Needed]
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+ - **Shared by [optional]:** [More Information Needed]
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+ - **Model type:** [More Information Needed]
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+ - **Language(s) (NLP):** [More Information Needed]
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+ - **License:** [More Information Needed]
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+ - **Finetuned from model [optional]:** [More Information Needed]
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+
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+ ### Model Sources [optional]
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+
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+ <!-- Provide the basic links for the model. -->
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+ - **Repository:** [More Information Needed]
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+ - **Paper [optional]:** [More Information Needed]
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+ - **Demo [optional]:** [More Information Needed]
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+
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+ ## Uses
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+ <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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+
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+ ### Direct Use
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+ <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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+ [More Information Needed]
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+ ### Downstream Use [optional]
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+ <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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+ [More Information Needed]
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+ ### Out-of-Scope Use
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+ <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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+ [More Information Needed]
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+ ## Bias, Risks, and Limitations
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+ <!-- This section is meant to convey both technical and sociotechnical limitations. -->
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+ [More Information Needed]
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+ ### Recommendations
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+ <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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+ Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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+
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+ ## How to Get Started with the Model
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+ Use the code below to get started with the model.
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+ [More Information Needed]
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+ ## Training Details
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+ ### Training Data
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+ <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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+ [More Information Needed]
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+ ### Training Procedure
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+ <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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+ #### Preprocessing [optional]
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+ [More Information Needed]
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+ #### Training Hyperparameters
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+ - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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+ #### Speeds, Sizes, Times [optional]
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+ <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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+ [More Information Needed]
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+ ## Evaluation
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+ <!-- This section describes the evaluation protocols and provides the results. -->
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+ ### Testing Data, Factors & Metrics
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+ #### Testing Data
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+ <!-- This should link to a Dataset Card if possible. -->
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+ [More Information Needed]
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+ #### Factors
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+ <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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+ #### Metrics
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+ <!-- These are the evaluation metrics being used, ideally with a description of why. -->
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+ ### Results
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+ #### Summary
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+ ## Model Examination [optional]
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+ <!-- Relevant interpretability work for the model goes here -->
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+ ## Environmental Impact
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+ <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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+ Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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+ - **Hardware Type:** [More Information Needed]
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+ - **Hours used:** [More Information Needed]
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+ - **Cloud Provider:** [More Information Needed]
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+ - **Compute Region:** [More Information Needed]
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+ - **Carbon Emitted:** [More Information Needed]
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+ ## Technical Specifications [optional]
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+ ### Model Architecture and Objective
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+ [More Information Needed]
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+ ### Compute Infrastructure
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+ #### Hardware
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+ #### Software
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+ ## Citation [optional]
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+ <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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+ **BibTeX:**
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+ **APA:**
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+ ## Glossary [optional]
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+ <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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+ ## More Information [optional]
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+ ## Model Card Authors [optional]
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+ ## Model Card Contact
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+ [More Information Needed]