Instructions to use benjamin/wtp-bert-tiny with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use benjamin/wtp-bert-tiny with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="benjamin/wtp-bert-tiny")# Load model directly from transformers import AutoModelForTokenClassification model = AutoModelForTokenClassification.from_pretrained("benjamin/wtp-bert-tiny", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7ea2f847238cf0d5cd5e4e769e4e62bf580a3b1a4114a8a961551e4dd0f120aa
- Size of remote file:
- 5.04 MB
- SHA256:
- 637d36365767bc84e98cdb22ffcd7e4f186a49cfda5b450ac27e377e218a6f17
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.