Update utils_models.py

utils_models.py

@@ -1,27 +1,27 @@
-import torch
-from transformers import AutoTokenizer, AutoModelForSequenceClassification
-
-def map_num_to_label(num):
-    return "сарказм" if num==1 else "не сарказм"
-
-def load_roberta(): 
-    model_ckpt = "ukr-roberta-base-finetuned-sarc"
-    tokenizer = AutoTokenizer.from_pretrained(model_ckpt)
-    id2label = {1: "sarcastic",0: "not_sarcastic"}
-    label2id = {"sarcastic": 1, "not_sarcastic": 0}
-    hf_model = AutoModelForSequenceClassification.from_pretrained(model_ckpt, num_labels=2, label2id=label2id, id2label=id2label)
-    return hf_model, tokenizer
-
-def predict_roberta(model, tokenizer, text):
-    tokenized_input = tokenizer(text, return_tensors="pt")
-    predictions = model(**tokenized_input)
-    prediction = predictions.logits.argmax().item()
-    return map_num_to_label(prediction)
-
-def identity_tokenizer(text):
-    return text
-
-def predict_lr_rf(model, vectorizer, text):
-    prediction = model.predict(vectorizer.transform([text]))
-    return map_num_to_label(prediction)
-
+import torch
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+
+def map_num_to_label(num):
+    return "сарказм" if num==1 else "не сарказм"
+
+def load_roberta(): 
+    model_ckpt = "Snizhanna/ukr-roberta-base-finetuned-sarc"
+    tokenizer = AutoTokenizer.from_pretrained(model_ckpt)
+    id2label = {1: "sarcastic",0: "not_sarcastic"}
+    label2id = {"sarcastic": 1, "not_sarcastic": 0}
+    hf_model = AutoModelForSequenceClassification.from_pretrained(model_ckpt, num_labels=2, label2id=label2id, id2label=id2label)
+    return hf_model, tokenizer
+
+def predict_roberta(model, tokenizer, text):
+    tokenized_input = tokenizer(text, return_tensors="pt")
+    predictions = model(**tokenized_input)
+    prediction = predictions.logits.argmax().item()
+    return map_num_to_label(prediction)
+
+def identity_tokenizer(text):
+    return text
+
+def predict_lr_rf(model, vectorizer, text):
+    prediction = model.predict(vectorizer.transform([text]))
+    return map_num_to_label(prediction)
+