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NusaBert-ner-v1.3
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View details ↗`t5-base-indonesian-summarization-cased` model is based on `t5-base-bahasa-summarization-cased` by huseinzol05, finetuned using id_liputan6 dataset.
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View details ↗Fine-tuned facebook/wav2vec2-large-xlsr-53 on the Indonesian Common Voice dataset. When using this model, make sure that your speech input is sampled at 16kHz. The model can be evaluated as follows on the Indonesian test data of Common Voice. The Common Voice `train`, `validation`, and ... datasets were used for training as well as ... and ... # TODO
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View details ↗whisper-medium-id
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View details ↗MorphEm
CaicedoLabMorphEm is a self supervised learning framework trained with the DINO Bag of Channels recipe on the entire CHAMMI-75 dataset. It serves as a benchmark for performance for self-supervised models. The model was pre-trained with a heterogenous dataset of microscopy images with the goal of obtaining cell morphology embeddings for biological applications. The primary use of this model is feature extraction of cellular morphology in image-based biological experiments. The model takes single-channel images as input and produces feature vectors with discriminative information of cellular phenotypes. The input images should be segmented ahead of time; this model does not identify the location of cells automatically. The feature embeddings have been tested in single-cell analysis problems. If the images of interest are multi-channel, each channel can be processed independently and then the feature embeddings of all channels are concatenated for downstream applications. The applications of the embeddings produced by this model include basic biology research, functional genomics studies, drug discovery projects, among others.
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View details ↗Swin2SR model that upscales images x2. It was introduced in the paper Swin2SR: SwinV2 Transformer for Compressed Image Super-Resolution and Restoration by Conde et al. and first released in this repository. Refer to the documentation.
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View details ↗Swin2SR model that upscales images x4. It was introduced in the paper Swin2SR: SwinV2 Transformer for Compressed Image Super-Resolution and Restoration by Conde et al. and first released in this repository.
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View details ↗zyi-256-flash
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View details ↗Eikos-27B
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View details ↗Eikos-4B
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View details ↗This is the official classifier for text behaviors in HarmBench. This model support standard (text) behaviors and contextual behaviors. 📚 Example Notebook to use the classifier can be found here 💻 *Table 1: Agreement rates between previous metrics and classifiers compared to human judgments on our manually labeled validation set. Our classifier, trained on distilled data from GPT-4-0613, achieves performance comparable to GPT-4.*
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View details ↗Zephyr_RMU
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View details ↗GRASP
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View details ↗chinese-poetry-gpt2
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