add stuff

diagnosis_generation/medalpaca/evaluate_model_2.py

@@ -16,28 +16,15 @@ device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
 #accelerator=Accelerator()
 #device=accelerator.device
 def run():
-	print(args.saved_model)
-	print(args.tokenizer)
 	logging.basicConfig(filename=args.log,  level=logging.DEBUG)
 	logging.info("performing evaluation")
 	logging.info("loading saved model")
-	
-	#checkpoint=torch.load(args.saved_model)
-	#model.load_state_dict(checkpoint)
-	#model=BioGptForCausalLM.from_pretrained(args.saved_model)
 	model=AutoModelForCausalLM.from_pretrained(args.saved_model)
-	#model=T5ForConditionalGeneration.from_pretrained("t5-small")
-	#model.load_state_dict(torch.load(args.saved_model))
-	#model=accelerator.load("./t5_small_deepspeed_train_test_2.pt")
-	#model=accelerator.load_state("./t5_small_deepspee_train_test.pt")
 	tokenizer=AutoTokenizer.from_pretrained(args.tokenizer)
 	tokenizer.padding_side="left"
 	tokenizer.pad_token=tokenizer.eos_token
 	model.config.pad_token_id=model.config.eos_token_id
 	test_dataset=preprocess(tokenizer, args.test_dataset)
-	#with open(args.test_dataset, "rb") as f:
-	#	data=pkl.load(f)
-	#test_dataset=DiagnosesDataset(data, tokenizer)
 	logging.info("running evaluation")
 	res=evaluate_model_loop(model, args.config_name, test_dataset, args.batch_size,tokenizer, args.topk, args.temp,  args.num_beams, args.early_stopping, args.no_rep_ngram, args.num_return_sequences, args.metrics, args.do_sample, args.generative, args.icd_codes)
 	logging.info(res)