Update README.md

README.md

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 ---
+tags:
+- text2text-generation
+
 license: mit
 ---
+datasets:
+- CohereForAI/aya_dataset
+
+
+license: mit
+---
+# Model Card of instructionMBERTv1 for Bertology
+
+A minimalistic instruction model with an already good analysed and pretrained encoder like roBERTa.
+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 training 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.
+
+## Training parameters
+
+- base model: "google-bert/bert-base-multilingual-cased"
+- trained for 8 epochs
+- batch size of 16
+- 20000 warm-up steps
+- learning rate of 0.0001
+
+## Purpose of instructionMBERT
+InstructionMBERT is intended for research purposes. The model-generated text should be treated as a starting point rather than a definitive solution for potential use cases. Users should be cautious when employing these models in their applications.