Fill-Mask
Transformers
PyTorch
Safetensors
Fairseq
Hebrew
roberta
hebrew
encoder
masked-language-modeling
mlm
named-entity-recognition
sentiment-analysis
monolingual
byte-level-bpe
Instructions to use HalleluBERT/HalleluBERT_large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HalleluBERT/HalleluBERT_large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="HalleluBERT/HalleluBERT_large")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("HalleluBERT/HalleluBERT_large") model = AutoModelForMaskedLM.from_pretrained("HalleluBERT/HalleluBERT_large") - Fairseq
How to use HalleluBERT/HalleluBERT_large with Fairseq:
from fairseq.checkpoint_utils import load_model_ensemble_and_task_from_hf_hub models, cfg, task = load_model_ensemble_and_task_from_hf_hub( "HalleluBERT/HalleluBERT_large" ) - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ab7e2bbe2594bbf195ca3534384f04a0b35658d292dfb6a4124674fb1f537188
- Size of remote file:
- 1.43 GB
- SHA256:
- 2bb88ad61a785ec5fdfb0b3c55afb0c2240321c4afda5c78f93de570e0077502
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