Instructions to use tbs17/MathBERT-custom with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tbs17/MathBERT-custom with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="tbs17/MathBERT-custom")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("tbs17/MathBERT-custom") model = AutoModelForMaskedLM.from_pretrained("tbs17/MathBERT-custom", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
-
Adding `safetensors` variant of this model
c3504a0
-
Pytorch example encoded_input fix (#1)
- Pytorch example encoded_input fix (7f37b8ae4e695eb4bf112857159f1eac25bfdd75) Co-authored-by: Dmitrii Ogurtsov <DimOgu@users.noreply.huggingface.co>db3e8ee -
Update README.md
632c8c4 -
Update README.md
92c41fd
-
Update README.md
10b6054 -
Update README.md
3e97117 -
Update README.md
019be46
-
remove customvocab file
d46c1c6 -
add mathvocab files
039f5a3
-
Create config.json
v19bd60dc -
Update README.md
v1.10288bf3 -
Create README.md
add v14ef51e6 -
add mathBERT-custom files
391ed79 -
initial commit
33fafa9