> word Wood dominated the embedding values, but these were supposed to go into 2 different categories
When faced with a similar challenge we developed a custom tokenizer, pretrained BERT base model[0], and finally a SPLADE-esque sparse embedding model[1] on top of that.
When faced with a similar challenge we developed a custom tokenizer, pretrained BERT base model[0], and finally a SPLADE-esque sparse embedding model[1] on top of that.
[0] - https://huggingface.co/atomic-canyon/fermi-bert-1024
[1] - https://huggingface.co/atomic-canyon/fermi-1024