Instructions to use KevinForm/bert-finetuned-ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KevinForm/bert-finetuned-ner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="KevinForm/bert-finetuned-ner")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("KevinForm/bert-finetuned-ner") model = AutoModelForTokenClassification.from_pretrained("KevinForm/bert-finetuned-ner", device_map="auto") - Notebooks
- Google Colab
- Kaggle
|
Download README.md from KevinForm/bert-finetuned-ner: direct link, hf CLI and curl.
- Browser
- Download file 1.62 kB
-
https://huggingface.co/KevinForm/bert-finetuned-ner/resolve/main/README.md
- Command line
-
hf download hf://KevinForm/bert-finetuned-ner/README.md
-
curl -L -o README.md https://huggingface.co/KevinForm/bert-finetuned-ner/resolve/main/README.md
1.62 kB
metadata
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: KevinForm/bert-finetuned-ner
results: []
KevinForm/bert-finetuned-ner
This model is a fine-tuned version of bert-base-cased on an unknown dataset. It achieves the following results on the evaluation set:
- Train Loss: 0.1831
- Validation Loss: 0.0644
- Epoch: 0
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: {'inner_optimizer': {'class_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 2631, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}}, 'dynamic': True, 'initial_scale': 32768.0, 'dynamic_growth_steps': 2000}
- training_precision: mixed_float16
Training results
| Train Loss | Validation Loss | Epoch |
|---|---|---|
| 0.1831 | 0.0644 | 0 |
Framework versions
- Transformers 4.16.2
- TensorFlow 2.6.2
- Datasets 1.18.4
- Tokenizers 0.11.6