Fill-Mask
Transformers
PyTorch
Safetensors
English
modernbert
ecommerce
e-commerce
retail
marketplace
shopping
amazon
ebay
alibaba
google
rakuten
bestbuy
walmart
flipkart
wayfair
shein
target
etsy
shopify
taobao
asos
carrefour
costco
overstock
pretraining
encoder
language-modeling
foundation-model
Instructions to use thebajajra/RexBERT-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use thebajajra/RexBERT-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="thebajajra/RexBERT-base")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("thebajajra/RexBERT-base") model = AutoModelForMaskedLM.from_pretrained("thebajajra/RexBERT-base") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4eaa483e57bf51e4438cb26d42320644757482fb639529a8e38b5fa92771582b
- Size of remote file:
- 599 MB
- SHA256:
- 329641d3341678cd130ef312c3ad031fa2b3a6c1968438f4883050fa7f51035d
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