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_base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HalleluBERT/HalleluBERT_base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="HalleluBERT/HalleluBERT_base")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("HalleluBERT/HalleluBERT_base") model = AutoModelForMaskedLM.from_pretrained("HalleluBERT/HalleluBERT_base") - Fairseq
How to use HalleluBERT/HalleluBERT_base 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_base" ) - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6605a022b104796d93b5ce6e18adb0882181ba2a81e85fa9aee35663a9b2e35e
- Size of remote file:
- 504 MB
- SHA256:
- 4ca32c2861c628d5622534c6d67c8c7ff4fb8d98a83599e005979477bbba22ee
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