Instructions to use Buseak/model_TrainTestSplit_berturk_v3_24Feb with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Buseak/model_TrainTestSplit_berturk_v3_24Feb with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Buseak/model_TrainTestSplit_berturk_v3_24Feb")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Buseak/model_TrainTestSplit_berturk_v3_24Feb") model = AutoModelForTokenClassification.from_pretrained("Buseak/model_TrainTestSplit_berturk_v3_24Feb", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7cb3d0ce1a7433361a5f73c1a8f18ffa64395d735c26815daaab34cd44336291
- Size of remote file:
- 3.52 kB
- SHA256:
- ff168c740be3231ff574739963ec3a3c7623e169e35a15ecfbe7e181ecc42453
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.