Token Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
Eval Results (legacy)
Instructions to use muibk/bert-finetuned-ner_TEST_HFCOURSE with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use muibk/bert-finetuned-ner_TEST_HFCOURSE with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="muibk/bert-finetuned-ner_TEST_HFCOURSE")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("muibk/bert-finetuned-ner_TEST_HFCOURSE") model = AutoModelForTokenClassification.from_pretrained("muibk/bert-finetuned-ner_TEST_HFCOURSE") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ec892d8a6f720d9b5ac49cd904c1fa2ce3eed9e1231a61ebeab9938fb639387c
- Size of remote file:
- 3.96 kB
- SHA256:
- 94478575ffbc6a303c7d6f2ddac66163f1cd1e775bf9b2643dcc84d6a2dc8e2b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.