Abstract: A collaboration platform provides a collaboration user interface (“UI”) through which a user can request a recommendation of an artificial intelligence (“AI”) model for performing entity extraction on documents in a document library maintained by the collaboration platform. In response to receiving such a request, the collaboration platform can select candidate documents from the documents in the library and process the candidate documents using AI models configured to extract entities from the one or more documents. The collaboration platform can then select one of the AI models based on the processing. The collaboration platform can also provide functionality for performing automated document tagging using term sets on documents maintained by the collaboration platform.

Abstract: Techniques configuring a machine learning model include instantiating a user interface configured to communicate with a machine learning model hosted on a collaborative computing platform. A selection of a file for input to the machine learning model, a selection of content in the file for input to the machine learning model, and instructions for applying the selected content to the machine learning model are received and sent to the machine learning model. A selection of one or more directories and an instruction to apply the machine learning model are sent to the machine learning model.

Abstract: Techniques configuring a machine learning model include receiving, via a user interface configured to communicate with a machine learning model hosted on a collaborative computing platform, a selection of a file for input to the machine learning model, a selection of content in the file for input to the machine learning model, and instructions for applying the selected content to the machine learning model, which are sent to the machine learning model. As new files are uploaded to the selected directories of the collaborative computing platform, the machine learning model is applied to the uploaded files to classify the files and extract metadata. The extracted metadata and associated classification data are stored in data structures associated with the new files. The data structures are existing data structures of the collaborative computing platform.

Abstract: Techniques configuring a machine learning model include receiving, via a user interface configured to communicate with a machine learning model hosted on a collaborative computing platform, a selection of a file for input to the machine learning model, a selection of content in the file for input to the machine learning model, and instructions for applying the selected content to the machine learning model, which are sent to the machine learning model. As new files are uploaded to the selected directories of the collaborative computing platform, the machine learning model is applied to the uploaded files to classify the files and extract metadata. The extracted metadata and associated classification data are stored in data structures associated with the new files. The data structures are existing data structures of the collaborative computing platform.

Abstract: Techniques configuring a machine learning model include instantiating a user interface configured to communicate with a machine learning model hosted on a collaborative computing platform. A selection of a file for input to the machine learning model, a selection of content in the file for input to the machine learning model, and instructions for applying the selected content to the machine learning model are received and sent to the machine learning model. A selection of one or more directories and an instruction to apply the machine learning model are sent to the machine learning model.

Abstract: In one example, an activity feed server may describe events in a project by collecting events from across multiple services into an activity feed personalized to the user. The activity feed server may store an event set describing activities related to the project. The activity feed server may rank a mature event set from the event set of events older than a period matching a processing delay based on a relevance weighting for a user to generate a curated event list. The activity feed server may queue a recent event set of events younger than the processing delay in chronological order to generate a recent event list. The activity feed server may generate an event list having the curated event list and the recent event list. The activity feed server may send the activity feed having the event list to a client device for presentation to the user.

Abstract: The present disclosure provides language-based mechanisms for generating acronyms from text input. The language of the text input may be provided or automatically detected. The target acronym length may indicate a maximum length and may vary depending on the input language. The text input may be separated into tokens and organized as a token tree list. Based on the tokens, an acronym may be generated from the available capital words. If there are not enough capital words, all words (e.g., both capitalized and lowercase words) may be used to generate the acronym. If there are not enough words, then all words and segments may be used to generate the acronym. Finally, a background color may be generated based characteristics relating to the text input or the generated acronym. The acronym and background color may be used to create a graphic, such as an icon or thumbnail, for a graphic user interface.

Abstract: The present disclosure provides language-based mechanisms for generating acronyms from text input. The language of the text input may be provided or automatically detected. The target acronym length may indicate a maximum length and may vary depending on the input language. The text input may be separated into tokens and organized as a token tree list. Based on the tokens, an acronym may be generated from the available capital words. If there are not enough capital words, all words (e.g., both capitalized and lowercase words) may be used to generate the acronym. If there are not enough words, then all words and segments may be used to generate the acronym. Finally, a background color may be generated based characteristics relating to the text input or the generated acronym. The acronym and background color may be used to create a graphic, such as an icon or thumbnail, for a graphic user interface.

Abstract: In one example, an activity feed server may describe events in a project by collecting events from across multiple services into an activity feed personalized to the user. The activity feed server may store an event set describing activities related to the project. The activity feed server may rank a mature event set from the event set of events older than a period matching a processing delay based on a relevance weighting for a user to generate a curated event list. The activity feed server may queue a recent event set of events younger than the processing delay in chronological order to generate a recent event list. The activity feed server may generate an event list having the curated event list and the recent event list. The activity feed server may send the activity feed having the event list to a client device for presentation to the user.