Update README.md

README.md

@@ -42,6 +42,8 @@ alt="instruction BERT drawing" width="600"/>
 
 A minimalistic instruction model with an already good analysed and pretrained encoder like BERT.
 So we can research the [Bertology](https://aclanthology.org/2020.tacl-1.54.pdf) with instruction-tuned models, [look at the attention](https://colab.research.google.com/drive/1mNP7c0RzABnoUgE6isq8FTp-NuYNtrcH?usp=sharing) and investigate [what happens to BERT embeddings during fine-tuning](https://aclanthology.org/2020.blackboxnlp-1.4.pdf).
+
+The trainings code is released at the [instructionBERT repository](https://gitlab.com/Bachstelze/instructionbert).
 We used the Huggingface API for [warm-starting](https://huggingface.co/blog/warm-starting-encoder-decoder) [BertGeneration](https://huggingface.co/docs/transformers/model_doc/bert-generation) with [Encoder-Decoder-Models](https://huggingface.co/docs/transformers/v4.35.2/en/model_doc/encoder-decoder) for this purpose.
 
 ## Run the model with a longer output
@@ -58,4 +60,13 @@ input = "Write a poem about love, peace and pancake."
 input_ids = tokenizer(input, return_tensors="pt").input_ids
 output_ids = model.generate(input_ids, max_new_tokens=200)
 print(tokenizer.decode(output_ids[0]))
-```
+```
+
+## Training parameters
+
+- base model: "bert-base-cased"
+- test subset of the Muennighoff/flan dataset
+- trained for 0.97 epochs
+- batch size of 14
+- 10000 warm-up steps
+- learning rate of 0.00005