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
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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
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curl -L -o README.md https://huggingface.co/KevinForm/bert-finetuned-ner/resolve/main/README.md
1.62 kB
| license: apache-2.0 | |
| tags: | |
| - generated_from_keras_callback | |
| model-index: | |
| - name: KevinForm/bert-finetuned-ner | |
| results: [] | |
| <!-- This model card has been generated automatically according to the information Keras had access to. You should | |
| probably proofread and complete it, then remove this comment. --> | |
| # KevinForm/bert-finetuned-ner | |
| This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/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 | |