Instructions to use avichr/Legal-heBERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use avichr/Legal-heBERT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="avichr/Legal-heBERT")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("avichr/Legal-heBERT") model = AutoModelForMaskedLM.from_pretrained("avichr/Legal-heBERT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Librarian Bot: Add base_model information to model
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# Legal-HeBERT
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Legal-HeBERT is a BERT model for Hebrew legal and legislative domains. It is intended to improve the legal NLP research and tools development in Hebrew. We release two versions of Legal-HeBERT. The first version is a fine-tuned model of [HeBERT](https://github.com/avichaychriqui/HeBERT) applied on legal and legislative documents. The second version uses [HeBERT](https://github.com/avichaychriqui/HeBERT)'s architecture guidlines to train a BERT model from scratch. <br>
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We continue collecting legal data, examining different architectural designs, and performing tagged datasets and legal tasks for evaluating and to development of a Hebrew legal tools.
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base_model: HeBERT
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# Legal-HeBERT
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Legal-HeBERT is a BERT model for Hebrew legal and legislative domains. It is intended to improve the legal NLP research and tools development in Hebrew. We release two versions of Legal-HeBERT. The first version is a fine-tuned model of [HeBERT](https://github.com/avichaychriqui/HeBERT) applied on legal and legislative documents. The second version uses [HeBERT](https://github.com/avichaychriqui/HeBERT)'s architecture guidlines to train a BERT model from scratch. <br>
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We continue collecting legal data, examining different architectural designs, and performing tagged datasets and legal tasks for evaluating and to development of a Hebrew legal tools.
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