Fill-Mask
Transformers
Safetensors
gemma3_text
feature-extraction
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
custom_code
text-generation-inference
Instructions to use thebajajra/RexGemma-Euro with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use thebajajra/RexGemma-Euro with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="thebajajra/RexGemma-Euro", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("thebajajra/RexGemma-Euro", trust_remote_code=True) model = AutoModel.from_pretrained("thebajajra/RexGemma-Euro", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e43a17b98db032da81132cd468045e9c2812740c020139d69439c83646d28be6
- Size of remote file:
- 1.07 GB
- SHA256:
- e9dc0cc7558bc128b11280fbdbacf630a260a637110ad69d3de2f03ca9650093
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