Instructions to use namesarnav/causalbench_math-t5-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use namesarnav/causalbench_math-t5-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="namesarnav/causalbench_math-t5-base")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("namesarnav/causalbench_math-t5-base") model = AutoModelForSequenceClassification.from_pretrained("namesarnav/causalbench_math-t5-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from namesarnav/causalbench_math-t5-base: direct link, hf CLI and curl.
- Browser
- Download file 5.91 kB
-
https://huggingface.co/namesarnav/causalbench_math-t5-base/resolve/main/training_args.bin
- Command line
-
hf download hf://namesarnav/causalbench_math-t5-base/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/namesarnav/causalbench_math-t5-base/resolve/main/training_args.bin
5.91 kB
- Xet hash:
- 0e7951c5abcde3d0fc19f618886cfb82bad5446a8657ec0945c73e341f82c7c2
- Size of remote file:
- 5.91 kB
- SHA256:
- 19ba945f1a2446a301b1b97357396f79c65cda5a0bf780ca5569114be84d000e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.