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
Adding `safetensors` variant of this model
#2
by SFconvertbot - opened
- model.safetensors +3 -0
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version https://git-lfs.github.com/spec/v1
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oid sha256:34438a9b15ea5e7a4c395ab99a96060c270af37fe76f917743b2a01fabace796
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size 47152312
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