# Load model directly
from transformers import AutoModelForSequenceClassification
model = AutoModelForSequenceClassification.from_pretrained("guyyanko/split-3-hebrew-trc-alephbert-base-EMP", trust_remote_code=True, dtype="auto")Quick Links
from transformers import TextClassificationPipeline
from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification
class TemporalRelationClassificationPipeline(TextClassificationPipeline):
def check_model_type(self, supported_models):
pass
pretrained_checkpoint = "guyyanko/split-3-hebrew-trc-alephbert-base-EMP"
model = AutoModelForSequenceClassification.from_pretrained(pretrained_checkpoint, trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained(pretrained_checkpoint, trust_remote_code=True)
classifier = pipeline(task='text-classification', model=model, tokenizer=tokenizer)
txt = "מחר [א1] אתאמן [/א1] אם [א2] אסיים [/א2] את כל המשימות שלי"
print(classifier(txt))
txt = "אחרי [א1] שאסיים [/א1] את כל המשימות שלי [א2] אלך [/א2] להתאמן בחדר הכושר"
print(classifier(txt))
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="guyyanko/split-3-hebrew-trc-alephbert-base-EMP", trust_remote_code=True)