# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("seninoseno/rubert-tiny-vacancy-information-extractor")
model = AutoModelForSequenceClassification.from_pretrained("seninoseno/rubert-tiny-vacancy-information-extractor", device_map="auto")Quick Links
RuBERT for vacancy information extraction
This is cointegrated/rubert-tiny model trained for vacancies sentences classification into 4 sections. Subject area of dataset - construction.
From MOAD.dev with <3
- Downloads last month
- 4
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="seninoseno/rubert-tiny-vacancy-information-extractor")