Instructions to use rjac/ner-distilbert-cased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rjac/ner-distilbert-cased with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="rjac/ner-distilbert-cased")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("rjac/ner-distilbert-cased") model = AutoModelForTokenClassification.from_pretrained("rjac/ner-distilbert-cased") - Notebooks
- Google Colab
- Kaggle
ner-distilber-cased
This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set:
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- optimizer: None
- training_precision: float32
Training results
Framework versions
- Transformers 4.20.1
- TensorFlow 2.9.1
- Datasets 2.3.2
- Tokenizers 0.12.1
- Downloads last month
- 8