OlmoEarth-v1-Base is a ViT-Base (89M parameters) foundation model for remote sensing tasks over Sentinel-1, Sentinel-2, and Landsat images or image time series. - GitHub: https://github.com/allenai/olmoearth_pretrain - Paper: https://allenai.org/papers/olmoearth If you are interested in computing embeddings with OlmoEarth, or fine-tuning OlmoEarth for a downstream task, see our tutorials:
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View details ↗OlmoEarth-v1-Tiny is a ViT-Tiny (6.2M parameters) foundation model for remote sensing tasks over Sentinel-1, Sentinel-2, and Landsat images or image time series. If you are interested in computing embeddings with OlmoEarth, or fine-tuning OlmoEarth for a downstream task, see our tutorials: The OlmoEarth model is deployed in the OlmoEarth Platform, a solution that enables any organization to leverage our models and accelerate their mission, without requiring deep AI or engineering expertise. Sign up to get updates and partner access.
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View details ↗This is the pretrained model presented in SciBERT: A Pretrained Language Model for Scientific Text, which is a BERT model trained on scientific text. The training corpus was papers taken from Semantic Scholar. Corpus size is 1.14M papers, 3.1B tokens. We use the full text of the papers in training, not just abstracts. SciBERT has its own wordpiece vocabulary (scivocab) that's built to best match the training corpus. We trained cased and uncased versions.
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View details ↗This is the pretrained model presented in SciBERT: A Pretrained Language Model for Scientific Text, which is a BERT model trained on scientific text. The training corpus was papers taken from Semantic Scholar. Corpus size is 1.14M papers, 3.1B tokens. We use the full text of the papers in training, not just abstracts. SciBERT has its own wordpiece vocabulary (scivocab) that's built to best match the training corpus. We trained cased and uncased versions.
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View details ↗SPECTER is a pre-trained language model to generate document-level embedding of documents. It is pre-trained on a powerful signal of document-level relatedness: the citation graph. Unlike existing pretrained language models, SPECTER can be easily applied to downstream applications without task-specific fine-tuning. If you're coming here because you want to embed papers, SPECTER has now been superceded by SPECTER2. Use that instead.
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