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--- |
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license: apache-2.0 |
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base_model: bert-base-cased |
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tags: |
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- generated_from_trainer |
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datasets: |
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- conll2003 |
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metrics: |
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- precision |
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- recall |
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- f1 |
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- accuracy |
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model-index: |
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- name: bert-finetuned-ner |
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results: |
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- task: |
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name: Token Classification |
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type: token-classification |
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dataset: |
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name: conll2003 |
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type: conll2003 |
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config: conll2003 |
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split: validation |
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args: conll2003 |
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metrics: |
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- name: Precision |
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type: precision |
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value: 0.9341149273447821 |
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- name: Recall |
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type: recall |
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value: 0.9520363513968361 |
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- name: F1 |
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type: f1 |
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value: 0.9429904984164028 |
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- name: Accuracy |
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type: accuracy |
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value: 0.9866515570730559 |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# bert-finetuned-ner |
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This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0741 |
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- Precision: 0.9341 |
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- Recall: 0.9520 |
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- F1: 0.9430 |
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- Accuracy: 0.9867 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 5 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
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| 0.0775 | 1.0 | 1756 | 0.0694 | 0.8912 | 0.9273 | 0.9089 | 0.9817 | |
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| 0.0377 | 2.0 | 3512 | 0.0707 | 0.9245 | 0.9445 | 0.9344 | 0.9850 | |
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| 0.0243 | 3.0 | 5268 | 0.0671 | 0.9281 | 0.9465 | 0.9372 | 0.9855 | |
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| 0.0145 | 4.0 | 7024 | 0.0734 | 0.9353 | 0.9507 | 0.9429 | 0.9859 | |
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| 0.006 | 5.0 | 8780 | 0.0741 | 0.9341 | 0.9520 | 0.9430 | 0.9867 | |
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### Framework versions |
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- Transformers 4.42.4 |
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- Pytorch 2.3.1+cu121 |
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- Datasets 2.20.0 |
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- Tokenizers 0.19.1 |
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### How to use and it's democase |
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from transformers import pipeline |
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model_checkpoint = "amannagrawall002/bert-finetuned-ner" |
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token_classifier = pipeline( |
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"token-classification", model=model_checkpoint, aggregation_strategy="simple" |
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) |
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print(token_classifier("My name is Sylvain and I work at Hugging Face in Brooklyn.")) |
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# [{'entity_group': 'PER', 'score': 0.9997023, 'word': 'Sylvain', 'start': 11, 'end': 18}, {'entity_group': 'ORG', 'score': 0.995275, 'word': 'Hugging Face', 'start': 33, 'end': 45}, {'entity_group': 'LOC', 'score': 0.9987465, 'word': 'Brooklyn', 'start': 49, 'end': 57}] |
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