Instructions to use Utshav/bert-finetuned-ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Utshav/bert-finetuned-ner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Utshav/bert-finetuned-ner")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Utshav/bert-finetuned-ner") model = AutoModelForTokenClassification.from_pretrained("Utshav/bert-finetuned-ner", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| license: apache-2.0 | |
| base_model: huggingface-course/bert-finetuned-ner | |
| tags: | |
| - generated_from_trainer | |
| metrics: | |
| - precision | |
| - recall | |
| - f1 | |
| - accuracy | |
| model-index: | |
| - name: bert-finetuned-ner | |
| results: [] | |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You | |
| should probably proofread and complete it, then remove this comment. --> | |
| # bert-finetuned-ner | |
| This model is a fine-tuned version of [huggingface-course/bert-finetuned-ner](https://huggingface.co/huggingface-course/bert-finetuned-ner) on an unknown dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.0731 | |
| - Precision: 0.9344 | |
| - Recall: 0.9524 | |
| - F1: 0.9433 | |
| - Accuracy: 0.9867 | |
| ## 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: | |
| - learning_rate: 2e-05 | |
| - train_batch_size: 8 | |
| - eval_batch_size: 8 | |
| - seed: 42 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: linear | |
| - num_epochs: 3 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | | |
| |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | |
| | 0.0273 | 1.0 | 1756 | 0.0761 | 0.9250 | 0.9424 | 0.9336 | 0.9849 | | |
| | 0.0183 | 2.0 | 3512 | 0.0671 | 0.9363 | 0.9505 | 0.9434 | 0.9865 | | |
| | 0.0077 | 3.0 | 5268 | 0.0731 | 0.9344 | 0.9524 | 0.9433 | 0.9867 | | |
| ### Framework versions | |
| - Transformers 4.38.0.dev0 | |
| - Pytorch 2.1.0+cu121 | |
| - Datasets 2.16.1 | |
| - Tokenizers 0.15.0 | |