Token Classification
Transformers
PyTorch
English
roberta
keyphrase-extraction
Eval Results (legacy)
Instructions to use ml6team/keyphrase-extraction-kbir-semeval2017 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ml6team/keyphrase-extraction-kbir-semeval2017 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="ml6team/keyphrase-extraction-kbir-semeval2017")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("ml6team/keyphrase-extraction-kbir-semeval2017") model = AutoModelForTokenClassification.from_pretrained("ml6team/keyphrase-extraction-kbir-semeval2017", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 652713092433449a453512d06ac5c3d4208a3ae41375d94eb17e386770929566
- Size of remote file:
- 1.42 GB
- SHA256:
- 9a1b44206d2c0596246fe574d2a33ea5eb7c5ea0512b5f27e3cee15a1baf4c4b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.