Text Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
text-embeddings-inference
Instructions to use Mahmoud8/bert-base-uncased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Mahmoud8/bert-base-uncased with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Mahmoud8/bert-base-uncased")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Mahmoud8/bert-base-uncased") model = AutoModelForSequenceClassification.from_pretrained("Mahmoud8/bert-base-uncased", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Mahmoud8/bert-base-uncased: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/Mahmoud8/bert-base-uncased/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Mahmoud8/bert-base-uncased/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Mahmoud8/bert-base-uncased/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- 0c4463a30bbc8bbc0f4c5b69085867c4d2402362fcfa21a2a7b8f9541786edde
- Size of remote file:
- 438 MB
- SHA256:
- e72fc9294c99a4e1fde87ff19f7b6cd74516abe258a398dd8be9c1036e1508fc
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