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--- |
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language: |
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- en |
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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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datasets: |
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- tner/ontonotes5 |
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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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widget: |
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- text: 'Hi! I am jack. I live in California and I work for Apple ' |
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example_title: Example 1 |
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- text: 'Thi book is amazing! I bought it on Amazon for 4$. ' |
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example_title: Example 2 |
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base_model: bert-base-cased |
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model-index: |
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- name: bert-finetuned-ner-ontonotes |
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results: |
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- task: |
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type: token-classification |
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name: Token Classification |
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dataset: |
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name: ontonotes5 |
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type: ontonotes5 |
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config: ontonotes5 |
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split: train |
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args: ontonotes5 |
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metrics: |
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- type: precision |
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value: 0.8567258883248731 |
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name: Precision |
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- type: recall |
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value: 0.8841595180407308 |
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name: Recall |
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- type: f1 |
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value: 0.8702265476459025 |
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name: F1 |
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- type: accuracy |
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value: 0.9754933764288157 |
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name: Accuracy |
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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-ontonotes |
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This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the ontonotes5 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1503 |
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- Precision: 0.8567 |
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- Recall: 0.8842 |
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- F1: 0.8702 |
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- Accuracy: 0.9755 |
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## Model description |
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Token classification experiment, NER, on business topics. |
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## Intended uses & limitations |
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The model can be used on token classification, in particular NER. It is fine tuned on business topic. |
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## Training and evaluation data |
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The dataset used is [ontonotes5](https://huggingface.co/datasets/tner/ontonotes5) |
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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: 6 |
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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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| 0.0842 | 1.0 | 7491 | 0.0950 | 0.8524 | 0.8715 | 0.8618 | 0.9745 | |
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| 0.0523 | 2.0 | 14982 | 0.1044 | 0.8449 | 0.8827 | 0.8634 | 0.9744 | |
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| 0.036 | 3.0 | 22473 | 0.1118 | 0.8529 | 0.8843 | 0.8683 | 0.9760 | |
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| 0.0231 | 4.0 | 29964 | 0.1240 | 0.8589 | 0.8805 | 0.8696 | 0.9752 | |
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| 0.0118 | 5.0 | 37455 | 0.1416 | 0.8570 | 0.8804 | 0.8685 | 0.9753 | |
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| 0.0077 | 6.0 | 44946 | 0.1503 | 0.8567 | 0.8842 | 0.8702 | 0.9755 | |
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### Framework versions |
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- Transformers 4.22.1 |
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- Pytorch 1.12.1+cu113 |
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- Datasets 2.5.1 |
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- Tokenizers 0.12.1 |
