>>> from transformers import pipeline
>>> protgpt2 = pipeline('text-generation', model="nferruz/ProtGPT2")
# length is expressed in tokens, where each token has an average length of 4 amino acids.
>>> sequences = protgpt2("<|endoftext|>", max_length=100, do_sample=True, top_k=950, repetition_penalty=1.2, num_return_sequences=10, eos_token_id=0)
>>> for seq in sequences:
        print(seq):
 {'generated_text': 'MINDLLDISRIISGKMTLDRAEVNLTAIARQVVEEQRQAAEAKSIQLLCSTPDTNHYVFG\nDFDRLKQTLWNLLSNAVKFTPSGGTVELELGYNAEGMEVYVKDSGIGIDPAFLPYVFDRF\nRQSDAADSRNYGGLGLGLAIVKHLLDLHEGNVSAQSEGFGKGATFTVLLPLKPLKRELAA\nVNRHTAVQQSAPLNDNLAGMKILIVEDRPDTNEMVSYILEEAGAIVETAESGAAALTSLK\nSYSPDLVLSDIGMPMMDGYEMIEYIREWKTTKGG'}
{'generated_text': 'MQGDSSISSSNRMFT\nLCKPLTVANETSTLSTTRNSKSNKRVSKQRVNLAESPERNAPSPASIKTNETEEFSTIKT\nTNNEVLGYEPNYVSYDFVPMEKCNLCNENCSIELASLNEETFVKKTICCHECRKKAIENA\nENNNTKGSAVSNNSVTSSSGRKKIIVSGSQILRNLDSLTSSKSNISTLLNPNHLAKLAKN\nGNLSSLSSLQSSASSISKSSSTSSTPTTSPKVSSPTNSPSSSPINSPTP'}
{'generated_text': 'M\nSTHVSLENTLASLQATFFSLEARHTALETQLLSTRTELAATKQELVRVQAEISRADAQAQ\nDLKAQILTLKEKADQAEVEAAAATQRAEESQAALEAQTAELAQLRLEKQAPQHVAEEGDP\nQPAAPTTQAQSPVTSAAAAASSAASAEPSKPELTFPAYTKRKPPTITHAPKAPTKVALNP\nSTLSTSGSGGGAKADPTPTTPVPSSSAGLIPKALRLPPPVTPAASGAKPAPSARSKLRGP\nDAPLSPSTQS'}
{'generated_text': 'MVLLSTGPLPILFLGPSLAELNQKYQVVSDTLLRFTNTV\nTFNTLKFLGSDS\n'}
{'generated_text': 'M\nNNDEQPFIMSTSGYAGNTTSSMNSTSDFNTNNKSNTWSNRFSNFIAYFSGVGWFIGAISV\nIFFIIYVIVFLSRKTKPSGQKQYSRTERNNRDVDSIKRANYYG\n'}
{'generated_text': 'M\nEAVYSFTITETGTGTVEVTPLDRTISGADIVYPPDTACVPLTVQPVINANGTWTLGSGCT\nGHFSVDTTGHVNCLTGGFGAAGVHTVIYTVETPYSGNSFAVIDVNVTEPSGPGDGGNGNG\nDRGDGPDNGGGNNPGPDPDPSTPPPPGDCSSPLPVVCSDRDCADFDTQAQVQIYLDRYGG\nTCDLDGNHDGTPCENLPNNSGGQSSDSGNGGGNPGTGSTHQVVTGDCLWNIASRNNGQGG\nQAWPALLAANNESITNP'}
{'generated_text': 'M\nGLTTSGGARGFCSLAVLQELVPRPELLFVIDRAFHSGKHAVDMQVVDQEGLGDGVATLLY\nAHQGLYTCLLQAEARLLGREWAAVPALEPNFMESPLIALPRQLLEGLEQNILSAYGSEWS\nQDVAEPQGDTPAALLATALGLHEPQQVAQRRRQLFEAAEAALQAIRASA\n'}
{'generated_text': 'M\nGAAGYTGSLILAALKQNPDIAVYALNRNDEKLKDVCGQYSNLKGQVCDLSNESQVEALLS\nGPRKTVVNLVGPYSFYGSRVLNACIEANCHYIDLTGEVYWIPQMIKQYHHKAVQSGARIV\nPAVGFDSTPAELGSFFAYQQCREKLKKAHLKIKAYTGQSGGASGGTILTMIQHGIENGKI\nLREIRSMANPREPQSDFKHYKEKTFQDGSASFWGVPFVMKGINTPVVQRSASLLKKLYQP\nFDYKQCFSFSTLLNSLFSYIFNAI'}
{'generated_text': 'M\nKFPSLLLDSYLLVFFIFCSLGLYFSPKEFLSKSYTLLTFFGSLLFIVLVAFPYQSAISAS\nKYYYFPFPIQFFDIGLAENKSNFVTSTTILIFCFILFKRQKYISLLLLTVVLIPIISKGN\nYLFIILILNLAVYFFLFKKLYKKGFCISLFLVFSCIFIFIVSKIMYSSGIEGIYKELIFT\nGDNDGRFLIIKSFLEYWKDNLFFGLGPSSVNLFSGAVSGSFHNTYFFIFFQSGILGAFIF\nLLPFVYFFISFFKDNSSFMKLF'}
{'generated_text': 'M\nRRAVGNADLGMEAARYEPSGAYQASEGDGAHGKPHSLPFVALERWQQLGPEERTLAEAVR\nAVLASGQYLLGEAVRRFETAVAAWLGVPFALGVASGTAALTLALRAYGVGPGDEVIVPAI\nTFIATSNAITAAGARPVLVDIDPSTWNMSVASLAARLTPKTKAILAVHLWGQPVDMHPLL\nDIAAQANLAVIEDCAQALGASIAGTKVGTFGDAAAFSFYPTKNMTTGEGGMLVTNARDLA\nQAARMLRSHGQDPPTAYMHSQVGFN'}

python run_clm.py --model_name_or_path nferruz/ProtGPT2 --train_file training.txt --validation_file validation.txt --tokenizer_name nferruz/ProtGPT2
 --do_train --do_eval --output_dir output --learning_rate 1e-06 

sequence='MGEAMGLTQPAVSRAVARLEERVGIRIFNRTARAITLTDEGRRFYEAVAPLLAGIEMHGYR\nVNVEGVAQLLELYARDILAEGRLVQLLPEWAD'

#Convert the sequence to a string like this
#(note we have to introduce new line characters every 60 amino acids,
#following the FASTA file format).

sequence = "<|endoftext|>\nMGEAMGLTQPAVSRAVARLEERVGIRIFNRTARAITLTDEGRRFYEAVAPLLAGIEMHGY\nRVNVEGVAQLLELYARDILAEGRLVQLLPEWAD\n<|endoftext|>"

# ppl function
def calculatePerplexity(sequence, model, tokenizer):
    input_ids = torch.tensor(tokenizer.encode(sequence)).unsqueeze(0) 
    input_ids = input_ids.to(device)
    with torch.no_grad():
        outputs = model(input_ids, labels=input_ids)
    loss, logits = outputs[:2]
    return math.exp(loss)

#And hence: 
ppl = calculatePerplexity(sequence, model, tokenizer)