Instructions to use RJ3vans/SSMNspanTagger with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RJ3vans/SSMNspanTagger with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="RJ3vans/SSMNspanTagger")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("RJ3vans/SSMNspanTagger") model = AutoModelForTokenClassification.from_pretrained("RJ3vans/SSMNspanTagger", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0c1b7d9d15f5e706481b977141df27a8c2e41b96fbd40d5b441105f34e94d60f
- Size of remote file:
- 1.33 GB
- SHA256:
- a875e3add4c4dc6f0d354d975e4b5e6b3bfb21708b5abf77ba389a81ec952a20
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.