Instructions to use maud-dr/baseline_3-seed_123 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use maud-dr/baseline_3-seed_123 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="maud-dr/baseline_3-seed_123")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("maud-dr/baseline_3-seed_123") model = AutoModelForSequenceClassification.from_pretrained("maud-dr/baseline_3-seed_123", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from maud-dr/baseline_3-seed_123: direct link, hf CLI and curl.
- Browser
- Download file 5.84 kB
-
https://huggingface.co/maud-dr/baseline_3-seed_123/resolve/main/training_args.bin
- Command line
-
hf download hf://maud-dr/baseline_3-seed_123/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/maud-dr/baseline_3-seed_123/resolve/main/training_args.bin
5.84 kB
- Xet hash:
- 0efb95b08859e8cc36cb6568fb9dfd4544b5799896d8bbae88e8a0c2ce658056
- Size of remote file:
- 5.84 kB
- SHA256:
- c0ba460f0d772241e885046bdc5b502f94f423091e768da931e0266bc88aef8d
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