Update README.md

README.md

@@ -27,23 +27,6 @@ pipeline_tag: fill-mask
 
 [Masked Diffusion Language Models (MDLMs)](arxiv.org/pdf/2406.07524), introduced by Sahoo et al, provide strong generative capabilities to BERT-style models. In this work, we pre-train and fine-tune ESM-2-150M protein language model (pLM) on the MDLM objective to scaffold functional motifs and unconditionally generate realistic, high-quality membrane protein sequences.
 
-## Model Usage
-
-MeMDLM leverages an internal backbone for generation, which is a fine-tuned version of the ESM-2-150M pLM. This backbone model can be accessed through this repo:
-
-```python
-from transformers import AutoTokenizer, AutoModelForMaskedLM
-
-tokenizer = AutoTokenizer.from_pretrained("ChatterjeeLab/MeMDLM")
-model = AutoModelForMaskedLM.from_pretrained("ChatterjeeLab/MeMDLM")
-
-input_sequence = "QMMALTFITYIGCGLSSIFLSVTLVILIQLCAALLLLNLIFLLDSWIALYnTRGFCIAVAVFLHYFLLVSFTWMGLEAFHMYLKFCIVGWGIPAVVVSIVLTISPDNYGidFCWINSNVVFYITVVGYFCVIFLLNVSMFIVVLVQLCRIKKKKQLGDL"
-
-inputs = tokenizer(input_sequence, return_tensors="pt")
-output = model(**inputs)
-
-filled_protein_seq = tokenizer.decode(output.squeeze()) # contains the output protein sequence with filled mask tokens
-```
 
 ## Repository Authors
 [Shrey Goel](mailto:[email protected]), Undergraduate Student at Duke University <br>