Update README.md

README.md

@@ -129,7 +129,7 @@ from transformers import AutoTokenizer, AutoModelForSequenceClassification
 import torch.nn.functional as F
 import json
 
-model_id = "./epfin"
+model_id = "gbyuvd/drugtargetpred-chemselfies"
 tokenizer = AutoTokenizer.from_pretrained(model_id)
 model = AutoModelForSequenceClassification.from_pretrained(model_id)
 
@@ -198,7 +198,7 @@ You can also use pipeline:
 ```python
 from transformers import pipeline
 
-classifier = pipeline("text-classification", model="./epfin")
+classifier = pipeline("text-classification", model="gbyuvd/drugtargetpred-chemselfies")
 classifier("[C] [C] [C] [C] [=C] [N] [C] [=C] [C] [=C] [C] [=C] [C] [=C] [C] [Ring1] [=Branch1] [=N] [C] [Ring1] [=Branch2] [=C] [Ring1] [=N] [C] [=C] [Ring1] [P] [C] [Ring2] [Ring1] [Branch1]") #Sempervirine (CID168919)
 # [{'label': 'LABEL_25', 'score': 0.5924742221832275}]
 
@@ -264,7 +264,7 @@ Adaptive Gradient Clipping = True
 I turned off the warm down, since in prior experiments it led to instability of losses in my case.
 For more information about Ranger21, you could check out [this repository](https://github.com/lessw2020/Ranger21).
 
-Final training epoch:
+Metrics at the last epoch (36th) :
 - Training Loss: 0.772700
 - Validation Loss: 1.299520
 - Accuracy: 0.619050