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")# 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
- Xet hash:
- 28ed93eeef32c100f52cf30a20f7a958d5d099a09fb3a417610627c6c81b70b9
- Size of remote file:
- 268 MB
- SHA256:
- f31a62e3a25a39aa9d032050687d207c7d4e73ab34f454f7ea250dfaf2f1a756
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.