bert-base-turkish-sentiment-cased

from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline

model = AutoModelForSequenceClassification.from_pretrained("savasy/bert-base-turkish-sentiment-cased")
tokenizer = AutoTokenizer.from_pretrained("savasy/bert-base-turkish-sentiment-cased")
sa = pipeline("sentiment-analysis", tokenizer=tokenizer, model=model)

p = sa("bu telefon modelleri çok kaliteli , her parçası çok özel bence")
print(p)
# [{'label': 'LABEL_1', 'score': 0.9871089}]
print(p[0]['label'] == 'LABEL_1')
# True

p = sa("Film çok kötü ve çok sahteydi")
print(p)
# [{'label': 'LABEL_0', 'score': 0.9975505}]
print(p[0]['label'] == 'LABEL_1')
# False

input_file = "/path/to/your/file/yourfile.tsv"

i, crr = 0, 0
for line in open(input_file):
    lines = line.strip().split("	")
    if len(lines) == 2:
        i = i + 1
        if i%100 == 0:
            print(i)
            
        pred = sa(lines[0])
        pred = pred[0]["label"].split("_")[1]
        
        if pred == lines[1]:
            crr = crr + 1

print(crr, i, crr/i)