Text Classification
Transformers
TensorBoard
Safetensors
mpnet
Generated from Trainer
text-embeddings-inference
Instructions to use HamidBekam/g-patentsbertav2-e2e with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HamidBekam/g-patentsbertav2-e2e with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="HamidBekam/g-patentsbertav2-e2e")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("HamidBekam/g-patentsbertav2-e2e") model = AutoModelForSequenceClassification.from_pretrained("HamidBekam/g-patentsbertav2-e2e", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 563582295984a103031c2306435eae2a99f1c647f6416c0ea0fe4b6fac8d8cf6
- Size of remote file:
- 5.97 kB
- SHA256:
- 0d36861ae477b864ca644a33f5a90335d6a74bb4d45fcfb9d3479e2d80db2b85
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.