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:
- 5f5627b9baecc129a93a045a1d4b2a3b12136481a61dfc4ac924b9fc8e7ff829
- Size of remote file:
- 1.43 GB
- SHA256:
- 0bc808a8b1ad01c589682613717fad3d43443d5a0739cdbc55694eeef84a71df
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.