--- base_model: - google/flan-t5-large datasets: - deepmind/math_dataset language: - en library_name: transformers metrics: - exact_match --- # Model Card for Model ID Welcome to the ⚖️📊CyberSolve LinAlg 1.1🦾📏 model card! We introduce **CyberSolve LinAlg 1.1**, a text-to-text large language model trained to solve linear equations. Specifically, *CyberSolve LingAlg 1.1* is a downstream version of the *FLAN-T5 large* model, [Google/FLAN-T5-large](https://huggingface.co/google/flan-t5-large), fine-tuned on the one-dimensional linear algebra split of the Google DeepMind mathematics dataset. **Note**: This is version **1.1**. The model card of the most updated version of CyberSolve LinAlg is available here: [CyberSolve LinAlg 1.2](https://huggingface.co/MarioBarbeque/CyberSolve-LinAlg-1.2) See also the most recent model demoed in the [CyberSolve LinAlg 1.2 🤖 Space](https://huggingface.co/spaces/MarioBarbeque/CyberSolveLinAlg1.2). ## Model Details ### Model Description This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses ### Direct Use [More Information Needed] ### Downstream Use [optional] [More Information Needed] ### Out-of-Scope Use [More Information Needed] ## Bias, Risks, and Limitations [More Information Needed] ### Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data [More Information Needed] ### Training Procedure #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] #### Speeds, Sizes, Times [optional] [More Information Needed] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data [More Information Needed] #### Factors [More Information Needed] #### Metrics [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] [More Information Needed] ## Environmental Impact 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). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]