Swahili-sentiment-Analysis-using-transformers VS huggingface_hub

Accuracy: common metric in sentiment analysis and classification, not the best one but denotes the fraction of times the model makes a correct prediction as compared to the total predictions it makes. Best used when the output variable is categorical or discrete. For example, how often a sentiment classification algorithm is correct.

I have copied the first news content from the Train.csv file to see how the Swahili model can work with it and it does the right classification because the sentence is long you can check on the notebook.

Something to think about:

https://github.com/huggingface/huggingface_hub

Yes! We have a well documented Python library (https://github.com/huggingface/huggingface_hub) and public endpoints (https://huggingface.co/docs/hub/api#endpoints-table) you can use to retrieve information about the models and potentially build UIs with specific use cases in mind

i think if you search for pixel art here there are some models worth checking out: https://huggingface.co/

I received another error saying OSError: We couldn't connect to 'https://huggingface.co' to load this model, couldn't find it in the cached files and it looks like ./vae is not the path to a directory containing a file named diffusion_pytorch_model.bin

I plan to release it on https://huggingface.co/, where all this cool AI stuff is available for free for everyone that wishes to try it.

For this I used mainly the chainner application with models from here but I also used the google colab automatic1111 stable diffusion webui (for example for Lanczos) and also spaces fromhuggingface like this one or then from the replicate.com website super resolution collection.

you will now need to create a huggingface account ( https://huggingface.co/) if you haven't already. When you have, go here and accept the terms, https://huggingface.co/runwayml/stable-diffusion-v1-5. When you have done both, click on your profile icon and go to settings. Click access tokens and then create token, name it whatever you want, select "write". When you are finished with all this, then you can run the next cell which is the hugging face cell. It will ask for a token, you copy and paste what you just created.