Instructions to use ditto123/model-tf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ditto123/model-tf with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="ditto123/model-tf")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("ditto123/model-tf") model = AutoModelForTokenClassification.from_pretrained("ditto123/model-tf", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tf_model.h5 from ditto123/model-tf: direct link, hf CLI and curl.
- Browser
- Download file 436 MB
-
https://huggingface.co/ditto123/model-tf/resolve/main/tf_model.h5
- Command line
-
hf download hf://ditto123/model-tf/tf_model.h5
-
curl -L -o tf_model.h5 https://huggingface.co/ditto123/model-tf/resolve/main/tf_model.h5
436 MB
- Xet hash:
- 182ee1668bdaeb8e331e2a19c3aae91d8014e1f94f775c532e9fa3df7dfb1cfe
- Size of remote file:
- 436 MB
- SHA256:
- 63eebe462bc26fe5c641738321f7218040b17086fe916424cde888ab14a277ca
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.