Instructions to use ClaireV/MLMA_Lab8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ClaireV/MLMA_Lab8 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="ClaireV/MLMA_Lab8")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("ClaireV/MLMA_Lab8") model = AutoModelForTokenClassification.from_pretrained("ClaireV/MLMA_Lab8", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tf_model.h5 from ClaireV/MLMA_Lab8: direct link, hf CLI and curl.
- Browser
- Download file 436 MB
-
https://huggingface.co/ClaireV/MLMA_Lab8/resolve/main/tf_model.h5
- Command line
-
hf download hf://ClaireV/MLMA_Lab8/tf_model.h5
-
curl -L -o tf_model.h5 https://huggingface.co/ClaireV/MLMA_Lab8/resolve/main/tf_model.h5
436 MB
- Xet hash:
- c1fc039322753822c36be08a1e873d370d303ac1c37066f4b59d15059d17ae72
- Size of remote file:
- 436 MB
- SHA256:
- f5ff18ff90211b6829a7502d321905fbdab0f7ee559f03701ce20d2e5abc50e8
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.