Instructions to use MRF18/results with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MRF18/results with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="MRF18/results")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("MRF18/results") model = AutoModelForSequenceClassification.from_pretrained("MRF18/results", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from MRF18/results: direct link, hf CLI and curl.
- Browser
- Download file 3.25 kB
-
https://huggingface.co/MRF18/results/resolve/main/training_args.bin
- Command line
-
hf download hf://MRF18/results/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/MRF18/results/resolve/main/training_args.bin
3.25 kB
- Xet hash:
- c28da271432e12ad8b38ff9c93e0adcccf28bb6a1febd55a1bdfdccd0d5a04c4
- Size of remote file:
- 3.25 kB
- SHA256:
- 55be306e0c71507a0f405f90b272e21141a801e31566881dd21de5aa852370e2
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.