Update README.md

README.md

@@ -44,3 +44,18 @@ A minimalistic instruction model with an already good analysed and pretrained en
 So we can research the [Bertology](https://aclanthology.org/2020.tacl-1.54.pdf) with instruction-tuned models and investigate [what happens to BERT embeddings during fine-tuning](https://aclanthology.org/2020.blackboxnlp-1.4.pdf).
 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
+
+```python
+from transformers import AutoTokenizer, EncoderDecoderModel
+
+# load the fine-tuned seq2seq model and corresponding tokenizer
+model_name = "Bachstelze/instructionBERTtest"
+model = EncoderDecoderModel.from_pretrained(model_name)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+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]))
+```