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--- |
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license: apache-2.0 |
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base_model: distilbert-base-uncased |
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tags: |
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- generated_from_trainer |
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datasets: |
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- ner |
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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-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: ner |
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type: ner |
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config: indian_names |
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split: train |
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args: indian_names |
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metrics: |
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- name: Precision |
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type: precision |
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value: 0.9896954662296407 |
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- name: Recall |
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type: recall |
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value: 0.9704150478224023 |
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- name: F1 |
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type: f1 |
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value: 0.9799604321344418 |
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- name: Accuracy |
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type: accuracy |
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value: 0.9894401834309103 |
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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-NER |
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This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the ner dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0320 |
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- Precision: 0.9897 |
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- Recall: 0.9704 |
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- F1: 0.9800 |
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- Accuracy: 0.9894 |
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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: 0.0001 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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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: 3 |
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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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| 0.0503 | 0.58 | 500 | 0.0506 | 0.9744 | 0.9656 | 0.9700 | 0.9846 | |
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| 0.0461 | 1.17 | 1000 | 0.0450 | 0.9781 | 0.9657 | 0.9719 | 0.9856 | |
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| 0.0428 | 1.75 | 1500 | 0.0424 | 0.9804 | 0.9677 | 0.9740 | 0.9864 | |
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| 0.0379 | 2.33 | 2000 | 0.0375 | 0.9839 | 0.9704 | 0.9771 | 0.9880 | |
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| 0.0352 | 2.91 | 2500 | 0.0320 | 0.9897 | 0.9704 | 0.9800 | 0.9894 | |
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### Framework versions |
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- Transformers 4.34.0 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.14.5 |
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- Tokenizers 0.14.1 |
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