Update README.md

README.md

@@ -42,9 +42,45 @@ However, this model can be used to interpret and analyse historical textual mate
 
 ## Model Dscription
 
+The following hyperparameters were used during training:
+- learning_rate: 3e-05
+- train_batch_size: 8
+- eval_batch_size: 8
+- seed: 42
+- gradient_accumulation_steps: 0
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 6
+- fp16: False
+
+Dataset:
+- Khubist2, which has been cleaned and chunked
+
+## Intended uses & limitations
+This model should primarly be used to fine-tune further on and downstream tasks.
+
+Inference for fill-mask with Huggingface Transformers in python:
+
+```python
+from transformers import pipeline
+
+summarizer = pipeline("fill-mask", model="Riksarkivet/bert-base-cased-swe-1800")
+historical_text = """Det vore [MASK] häller nödvändigt att bita af tungan än berättat hvad jag varit med om."""
+print(summarizer(historical_text))
+```
+
+
+
 
 
 ## Acknowledgements
+We gratefully acknowledge EuroHPC (https://eurohpc-ju.europa.eu) for funding this research by providing computing resources of the HPC system Vega at the Institute of Information Science (https://www.izum.si)
+and Språkbanken (Swe-Clarin) for the datasets.
+
+## Citation Information
+
+Eva Pettersson and Lars Borin (2022)
+Swedish Diachronic Corpus
+In Darja Fišer & Andreas Witt (eds.), CLARIN. The Infrastructure for Language Resources. Berlin: deGruyter. https://degruyter.com/document/doi/10.1515/9783110767377-022/html
 
-We gratefully acknowledge the HPC RIVR consortium (https://www.hpc-rivr.si) and EuroHPC JU (https://eurohpc-ju.europa.eu) for funding this research by providing computing resources of the HPC system Vega at the Institute of Information Science (https://www.izum.si).