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README.md
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---
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model-index:
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- name: RNAMamba-14M
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results: []
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license: apache-2.0
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datasets:
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- afg1/rnacentral_subset
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pipeline_tag: fill-mask
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inference: false
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---
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# RNAMamba-14M
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This model is a
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It is trained with a masked language modelling objective, and a context size of 8,192 nucleotides.
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The [dataset](https://huggingface.co/datasets/afg1/rnacentral_subset) has sequences ranging in length from 10 to 8192, so the model should be pretty good at handling sequences in that range.
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This is a deliberately small model with only 14.1 million parameters (8 hidden layers, hidden dim 512, intermediate size 1024) such that it will run fast without a GPU. We may train something bigger if it looks like these embeddings are not good enough.
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<!--## Model description
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I'll fill this in later...
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## Intended uses & limitations
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More information needed
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## Training procedure
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-->
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### Training hyperparameters
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- Transformers 4.39.3
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- Pytorch 2.2.2+cu118
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- Datasets 2.18.0
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- Tokenizers 0.15.2
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---
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tags:
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- generated_from_trainer
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model-index:
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- name: RNAMamba-14M
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results: []
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# RNAMamba-14M
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This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training procedure
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### Training hyperparameters
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- Transformers 4.39.3
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- Pytorch 2.2.2+cu118
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- Datasets 2.18.0
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- Tokenizers 0.15.2
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model.safetensors
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training_args.bin
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