--- license: apache-2.0 language: - ru metrics: - bleu pipeline_tag: image-to-text widget: - src: https://huggingface.co/dumperize/movie-picture-captioning/resolve/main/vertical_15x.jpeg example_title: Custom Image Sample 1 --- # Model Card for Model ID This model generate a description for movie posters ... mm, in principle, for any photo. # Model Details ## Model Description This is an encoder decoder model based on [VisionEncoderDecoderModel](https://huggingface.co/docs/transformers/model_doc/vision-encoder-decoder). [Google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) was used as encoder, [DeepPavlov/rubert-base-cased](https://huggingface.co/DeepPavlov/rubert-base-cased) as decoder. We refined the model on the dataset with descriptions and movie posters by russian app Kinoposk. Now the model generates descriptions on the jargon of blockbusters =). ## Model Sources - **Repository:** [github.com/slivka83](https://github.com/slivka83/) - **Demo [optional]:** [@MPC_project_bot](https://t.me/MPC_project_bot) # Uses ## Direct Use [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. # Training Details ## Training Data We compiled a dataset from the open source of all Russian-language films for October 2022 - [kinopoisk](https://www.kinopoisk.ru/). Films with very short or very long descriptions were not included in the dataset, films with blank or very small images were excluded too. ### Preprocessing The model was trained on 8 16 GB V100 for 90 hours. # 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] # How to Get Started with the Model Use the code below to get started with the model.
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