Instructions to use FritzOS/TEdetection_distilBERT_NER_final with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use FritzOS/TEdetection_distilBERT_NER_final with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="FritzOS/TEdetection_distilBERT_NER_final")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("FritzOS/TEdetection_distilBERT_NER_final") model = AutoModelForTokenClassification.from_pretrained("FritzOS/TEdetection_distilBERT_NER_final", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from FritzOS/TEdetection_distilBERT_NER_final: direct link, hf CLI and curl.
- Browser
- Download file 1.7 kB
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https://huggingface.co/FritzOS/TEdetection_distilBERT_NER_final/resolve/main/README.md
- Command line
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hf download hf://FritzOS/TEdetection_distilBERT_NER_final/README.md
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curl -L -o README.md https://huggingface.co/FritzOS/TEdetection_distilBERT_NER_final/resolve/main/README.md
1.7 kB
| license: apache-2.0 | |
| tags: | |
| - generated_from_keras_callback | |
| model-index: | |
| - name: TEdetection_distiBERT_NER_final | |
| 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. --> | |
| # TEdetection_distiBERT_NER_final | |
| This model is a fine-tuned version of [FritzOS/TEdetection_distiBERT_mLM_final](https://huggingface.co/FritzOS/TEdetection_distiBERT_mLM_final) on an unknown dataset. | |
| It achieves the following results on the evaluation set: | |
| - Train Loss: 0.0031 | |
| - Validation Loss: 0.0035 | |
| - 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: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 220743, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} | |
| - training_precision: float32 | |
| ### Training results | |
| | Train Loss | Validation Loss | Epoch | | |
| |:----------:|:---------------:|:-----:| | |
| | 0.0031 | 0.0035 | 0 | | |
| ### Framework versions | |
| - Transformers 4.19.4 | |
| - TensorFlow 2.8.2 | |
| - Datasets 2.2.2 | |
| - Tokenizers 0.12.1 | |