Fill-Mask
Transformers
PyTorch
Safetensors
Hebrew
bert
hebrew
nlp
masked-language-model
BERT
parliamentary-proceedings
language-model
Knesset
DictaBERT
fine-tuning
Instructions to use GiliGold/Knesset-DictaBERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use GiliGold/Knesset-DictaBERT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="GiliGold/Knesset-DictaBERT")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("GiliGold/Knesset-DictaBERT") model = AutoModelForMaskedLM.from_pretrained("GiliGold/Knesset-DictaBERT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from GiliGold/Knesset-DictaBERT: direct link, hf CLI and curl.
- Browser
- Download file 738 MB
-
https://huggingface.co/GiliGold/Knesset-DictaBERT/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://GiliGold/Knesset-DictaBERT/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/GiliGold/Knesset-DictaBERT/resolve/main/pytorch_model.bin
738 MB
- Xet hash:
- c93c5877e4308b2c77974a5f42556dfa9be3910c42e8cf53699eb903c9a08381
- Size of remote file:
- 738 MB
- SHA256:
- 0f39c8ed70496f8542f1d68268d49f7193ceeb9fad26f81601a2550eb59a17b2
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.