Instructions to use P829692/thesis-model-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use P829692/thesis-model-1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="P829692/thesis-model-1")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("P829692/thesis-model-1") model = AutoModelForTokenClassification.from_pretrained("P829692/thesis-model-1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from P829692/thesis-model-1: direct link, hf CLI and curl.
- Browser
- Download file 3.52 kB
-
https://huggingface.co/P829692/thesis-model-1/resolve/main/training_args.bin
- Command line
-
hf download hf://P829692/thesis-model-1/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/P829692/thesis-model-1/resolve/main/training_args.bin
3.52 kB
- Xet hash:
- 7eb78f29188e3b3a135c052f2ce7a6c5959612e43489155aa513be34016b4333
- Size of remote file:
- 3.52 kB
- SHA256:
- d14645340ea9e4938be045600cbec1f3595b46c2818ce608f73cbf396a631af7
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