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Pre-trained RNN-T model for MLPerf Inference
", "publication_date": "2020-02-11", "publisher": "Zenodo", "resource_type": { "id": "other", "title": { "de": "Sonstige", "en": "Other" } }, "rights": [ { "description": { "en": "The Creative Commons Attribution license allows re-distribution and re-use of a licensed work on the condition that the creator is appropriately credited." }, "icon": "cc-by-icon", "id": "cc-by-4.0", "props": { "scheme": "spdx", "url": "https://creativecommons.org/licenses/by/4.0/legalcode" }, "title": { "en": "Creative Commons Attribution 4.0 International" } } ], "title": "Pre-trained RNN-T model", "version": "0.0.0" }, "parent": { "access": { "owned_by": { "user": 90789 } }, "communities": { "default": "505bea1d-f855-4e84-a521-af4010e76619", "entries": [ { "access": { "member_policy": "open", "members_visibility": "public", "record_policy": "open", "review_policy": "open", "visibility": "public" }, "children": { "allow": false }, "created": "2018-11-07T22:32:18.671160+00:00", "custom_fields": {}, "deletion_status": { "is_deleted": false, "status": "P" }, "id": "505bea1d-f855-4e84-a521-af4010e76619", "links": {}, "metadata": { "curation_policy": "Please follow the guidelines provided on the main page for submitting model weights to MLPerf.
\r\n", "description": "MLPerf is a community-led broad ML benchmark suite for measuring the performance of ML software frameworks, ML hardware accelerators, and ML cloud and edge platforms. For additional information on MLPerf, please visit mlperf.org.", "page": "MLPerf is a community-led broad ML benchmark suite for measuring the performance of ML software frameworks, ML hardware accelerators, and ML cloud and edge platforms. For additional information on MLPerf, please visit mlperf.org.
\r\n\r\nTo be involved in the activities surrounding MLPerf and its discussions, you may be interested in joining the following MLPerf working groups, which are identified below:
\r\n\r\nThe Zenodo MLPerf community hosts the models that will be used for inference benchmarking.
\r\n\r\nIf you wish to submit a model, please follow the guidelines provided below when you upload a model to the MLPerf community. The submitted model will be reviewed by the curator to ensure it follows the standards/conventions and approved/rejected. So, we strongly recommend following the below guidelines.
\r\n\r\nPlease follow the following guidelines when you upload the files:
\r\n\r\nOn the upload form, there are multiple sections (e.g. Upload type, Basic information, etc.). For each of those sections, kindly follow the guidelines provided below.
\r\n\r\nNot following the guidelines can cause the curator to reject your upload.
\r\n\r\nChoose “Other”
\r\n\r\nTitle: "Trained Model for <NN Model name> for MLPerf Inference"
\r\n\r\nAuthors: Enter all the authors that have helped contribute to the work.
\r\n\r\nDescription: We recommend filling in the "Description" field with the below information:
\r\n\r\nVersion: MLPerf v0.5 <Training>/<Inference>
\r\n\r\nKeywords: Add the followings to keywords <Application>, <ML Task>, <Framework>, <Dataset>, <Training/Inference>, Pretrained Model
\r\n\r\nAdditional Notes: (This is optional, put whatever additional information here).
\r\n\r\nPlease select the following:
\r\n\r\n