Add training data and summary of evaluation results.

README.md

@@ -2,7 +2,7 @@
 license: apache-2.0
 ---
 
-# mBERT swedish distileld base model (cased)
+# mBERT swedish distilled base model (cased)
 
 This model is a distilled version of [mBERT](https://huggingface.co/bert-base-multilingual-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*.
 
@@ -14,3 +14,18 @@ This is a 6-layer version of mBERT, having been distilled using the [LightMBERT]
 ## 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 9 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 0.859 which is competitive with the score mBERT obtained, 0.866.
+
+When evaluated on the [English WikiANN](https://huggingface.co/datasets/wikiann) dataset, it achieved an average F1 score of 0.826 which is competitive with the score mBERT obtained, 0.849. 
+
+Additional results and comparisons are presented in my Master's Thesis
+