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README.md

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+language: Swedish
 license: apache-2.0
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+
+# KB-BERT distilled base model (cased)
+
+This model is a distilled version of [KB-BERT](https://huggingface.co/KB/bert-base-swedish-cased). It was distilled using Swedish data, the 2010-2015 portion of the [Swedish Culturomics Gigaword Corpus](https://spraakbanken.gu.se/en/resources/gigaword). The code for the distillation process can be found [here](https://github.com/AddedK/swedish-mbert-distillation/blob/main/azureML/pretrain_distillation.py). This was done as part of my Master's Thesis: *Task-agnostic knowledge distillation of mBERT to Swedish*.
+
+
+## Model description
+This is a 6-layer version of KB-BERT, having been distilled using the [LightMBERT](https://arxiv.org/abs/2103.06418) distillation method, but without freezing the embedding layer.
+
+
+## Intended uses & limitations
+You can use the raw model for either masked language modeling or next sentence prediction, but it's mostly intended to
+be fine-tuned on a downstream task. 
+
+
+## Training data
+
+The data used for distillation was the 2010-2015 portion of the [Swedish Culturomics Gigaword Corpus](https://spraakbanken.gu.se/en/resources/gigaword).
+The tokenized data had a file size of approximately 7.4 GB.
+
+## Evaluation results
+
+When evaluated on the [SUCX 3.0 ](https://huggingface.co/datasets/KBLab/sucx3_ner) dataset, it achieved an average F1 score of 0.887 which is competitive with the score KB-BERT obtained, 0.894. 
+
+Additional results and comparisons are presented in my Master's Thesis