The HuggingFace text classification template is now available to users. You can use the pre-configured text classifier model template to train with your data and improve the accuracy of your results.
To use the template, when you create a model, choose Text Classifier > Template > Select "HuggingFace".
Text classification is a common NLP task that assigns a label or class to text. Some of the largest companies run text classification in production for a wide range of practical applications. One of the most popular forms of text classification is sentiment analysis, which assigns a label like 🙂 positive, 🙁 negative, or 😐 neutral to a sequence of text.
The silent face anti-spoofing detection model is used to determine if the face in an image is a spoofing attempt or not.
It is designed to prevent people from tricking facial identification systems, such as those used for unlocking phones or accessing secure locations. This is achieved through a process called "liveness" or "anti-spoofing" which judges whether the face presented is genuine or not.
The face presented by other media can be defined as a fake: photo prints of faces, faces on phone screens, silicone masks, 3D human images, etc. This model outputs three concepts:
We've made major improvements to the look and feel of Clarifai Community! There's a whole new look when navigating the listing pages for models and workflows, we've added new designs and colors for model and workflow icons, and you can now view them in list or grid mode.
We've replaced “Stored Inputs” with “Average Stored Inputs" and now compute it as an average, not as the total. We've also added the word “Total” to “Training Hours” and “Ops Count.” They are now referred to as “Total Training Hours” and “Total Ops Count” respectively.
Some of the menus have collapsible sidebars; in the collapsed view on the left sidebar, the menu items are hidden and only the icons are left. We have now added a hover pop-up of the same text we hide to help users understand what each menu item is for.
Public profiles show a user's name if it's their own account, they are a collaborator of your app, or they are part of your organization. Otherwise, it only shows the account name.
The workflow editor is now able to interpret an entire workflow graph definition and dynamically calculate the proper 2-dimensional coordinates for each of the nodes in the graph.
You can now use the collection of small Python functions and classes to make common patterns shorter and easier when interacting with our API.
model_type_id of the model was trainable, then a new ModelVersion was created with UNTRAINED status by default. Otherwise, if the model_type_id was not trainable, then a new ModelVersion was created with TRAINED status.output_info, a new trained model version was created with the new info. Otherwise, if you were patching a trainable model where the latest model version had not been trained, the created model version was marked as untrained by default. If you were patching an untrainable model type, the new created model version was marked as trained.*_info fields in the model version—to effect training.*_info fields—such as output_info, input_info, train_info, and import_info—will be migrated to the endpoint. This would minimize the confusion and difficulty of maintaining these endpoints. Users will be able patch model specific fields without worrying about model version fields being affected.output_info (not including concepts).
