Instructions to use seninoseno/rubert-tiny-vacancy-information-extractor with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use seninoseno/rubert-tiny-vacancy-information-extractor with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="seninoseno/rubert-tiny-vacancy-information-extractor")# 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") - Notebooks
- Google Colab
- Kaggle
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
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