Instructions to use dmahata/dlkp_test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dmahata/dlkp_test with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="dmahata/dlkp_test")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("dmahata/dlkp_test") model = AutoModelForTokenClassification.from_pretrained("dmahata/dlkp_test") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4eba0143cde0863d9dd6d50367be0b5e2ea5eb333ede74bc064b16eead288f4e
- Size of remote file:
- 496 MB
- SHA256:
- ca902a8ce94ba2a083fbe6a89886220532a82b9d7cb0ff303abfcaf769f1e2e4
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.