Token Classification
Transformers
Safetensors
English
Hebrew
xlm-roberta
code-switching
hebrew
transliteration
romanization
text-to-speech
Eval Results (legacy)
Instructions to use Hebrish/Hebrew-Latin-Token-Classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Hebrish/Hebrew-Latin-Token-Classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Hebrish/Hebrew-Latin-Token-Classifier")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Hebrish/Hebrew-Latin-Token-Classifier") model = AutoModelForTokenClassification.from_pretrained("Hebrish/Hebrew-Latin-Token-Classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Copy of danielrosehill/hebrew-latin-token-classifier @ 01f092f1 (DOI 10.57967/hf/10997; DOI repos cannot be moved)
e303bc1 verified Download tokenizer.json from Hebrish/Hebrew-Latin-Token-Classifier: direct link, hf CLI and curl.
- Browser
- Download file 17.1 MB
-
https://huggingface.co/Hebrish/Hebrew-Latin-Token-Classifier/resolve/main/tokenizer.json
- Command line
-
hf download hf://Hebrish/Hebrew-Latin-Token-Classifier/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/Hebrish/Hebrew-Latin-Token-Classifier/resolve/main/tokenizer.json
17.1 MB
- Xet hash:
- 4558f350f384fcb92728f0d5032c216dbdf9453bb9cce6f3945db291f358a3cc
- Size of remote file:
- 17.1 MB
- SHA256:
- 3ed3aba76839292e668a3726cf01dfaf90cbc141502955998c4bc233aac28ce6
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.