Instructions to use mrdantzig/results with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mrdantzig/results with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mrdantzig/results")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("mrdantzig/results") model = AutoModelForSequenceClassification.from_pretrained("mrdantzig/results", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from mrdantzig/results: direct link, hf CLI and curl.
- Browser
- Download file 5.14 kB
-
https://huggingface.co/mrdantzig/results/resolve/main/training_args.bin
- Command line
-
hf download hf://mrdantzig/results/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/mrdantzig/results/resolve/main/training_args.bin
5.14 kB
- Xet hash:
- 4707acd43afb7c5543898828dce0202b3a8d7eecbe50eaa8bc94e97bb92df166
- Size of remote file:
- 5.14 kB
- SHA256:
- e528b2761e1cf589035fce7136709c970cf74388f1476117c93781735977eec2
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