Text Classification
Transformers
PyTorch
TensorBoard
Safetensors
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use rootacess/distilbert-base-uncased-finetuned-mathQA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rootacess/distilbert-base-uncased-finetuned-mathQA with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="rootacess/distilbert-base-uncased-finetuned-mathQA")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("rootacess/distilbert-base-uncased-finetuned-mathQA") model = AutoModelForSequenceClassification.from_pretrained("rootacess/distilbert-base-uncased-finetuned-mathQA", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from rootacess/distilbert-base-uncased-finetuned-mathQA: direct link, hf CLI and curl.
- Browser
- Download file 3.52 kB
-
https://huggingface.co/rootacess/distilbert-base-uncased-finetuned-mathQA/resolve/refs%2Fpr%2F1/training_args.bin
- Command line
-
hf download hf://rootacess/distilbert-base-uncased-finetuned-mathQA@refs/pr/1/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/rootacess/distilbert-base-uncased-finetuned-mathQA/resolve/refs%2Fpr%2F1/training_args.bin
3.52 kB
- Xet hash:
- b15dcff2d87ed3e2ae6b19936286c81124daea14e2bb9026b920dd300f18825b
- Size of remote file:
- 3.52 kB
- SHA256:
- f2e78d147d891c0eaf3450cc229b09c15e506aff9cf4fb710517a6c66098b4f4
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.