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Heavily inspired by google/gemma-scope we are releasing a suite of 8 sparse autoencoders for laion/CLIP-ViT-L-14-laion2B-s32B-b82K. Training logs are available via wandb and training code is available on github. The training process is heavily reliant on AWS ECS so the weights and biases logs may contain some strange artifacts when a spot instance is killed and the training is resumed by another instance. Some of the code is ripped directly from Hugo Fry. We calculate MSE as `(batch - reconstruction).pow(2).sum(dim=-1).mean()` i.e. The MSE between the batch and the reconstruction, summed across features. We use batch norm to bring all activations into a similar range. We normalize the input before training using batch norm.

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This model is a fine-tuned version of neuralmind/bert-base-portuguese-cased It achieves the following results on the evaluation set: - Loss: 0.1140 - Precision: 0.9147 - Recall: 0.9483 - F1: 0.9312 - Accuracy: 0.9700 This model was fine-tunned on token classification task (NER) on Portuguese archival documents. The annotated labels are: Date, Profession, Person, Place, Organization All the training and evaluation data is available at: http://ner.epl.di.uminho.pt/

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