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--- |
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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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- 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: my_awesome_wnut_model |
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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: test |
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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: 1.0 |
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- name: Recall |
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type: recall |
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value: 1.0 |
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- name: F1 |
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type: f1 |
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value: 1.0 |
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- name: Accuracy |
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type: accuracy |
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value: 1.0 |
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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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# my_awesome_wnut_model |
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This model is a fine-tuned version of [bert-base-cased](https://huggingface.co./bert-base-cased) on the ner dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0000 |
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- Precision: 1.0 |
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- Recall: 1.0 |
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- F1: 1.0 |
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- Accuracy: 1.0 |
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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: 5e-05 |
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- train_batch_size: 32 |
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- eval_batch_size: 32 |
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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: 50 |
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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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| No log | 1.0 | 344 | 0.0000 | 1.0000 | 1.0000 | 1.0000 | 1.0000 | |
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| 0.0027 | 2.0 | 688 | 0.0000 | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0019 | 3.0 | 1032 | 0.0001 | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0019 | 4.0 | 1376 | 0.0000 | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0021 | 5.0 | 1720 | 0.0000 | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0016 | 6.0 | 2064 | 0.0000 | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0016 | 7.0 | 2408 | 0.0000 | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0007 | 8.0 | 2752 | 0.0000 | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.001 | 9.0 | 3096 | 0.0000 | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.001 | 10.0 | 3440 | 0.0000 | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.001 | 11.0 | 3784 | 0.0001 | 1.0000 | 1.0000 | 1.0000 | 1.0000 | |
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| 0.0008 | 12.0 | 4128 | 0.0000 | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0008 | 13.0 | 4472 | 0.0000 | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0007 | 14.0 | 4816 | 0.0000 | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0009 | 15.0 | 5160 | 0.0000 | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0006 | 16.0 | 5504 | 0.0000 | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0006 | 17.0 | 5848 | 0.0000 | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0003 | 18.0 | 6192 | 0.0000 | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0006 | 19.0 | 6536 | 0.0000 | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0006 | 20.0 | 6880 | 0.0000 | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0007 | 21.0 | 7224 | 0.0000 | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0007 | 22.0 | 7568 | 0.0000 | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0007 | 23.0 | 7912 | 0.0000 | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0005 | 24.0 | 8256 | 0.0000 | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0001 | 25.0 | 8600 | 0.0000 | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0001 | 26.0 | 8944 | 0.0000 | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0002 | 27.0 | 9288 | 0.0000 | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0003 | 28.0 | 9632 | 0.0000 | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0003 | 29.0 | 9976 | 0.0000 | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0001 | 30.0 | 10320 | 0.0000 | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0 | 31.0 | 10664 | 0.0000 | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0001 | 32.0 | 11008 | 0.0000 | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0001 | 33.0 | 11352 | 0.0000 | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0001 | 34.0 | 11696 | 0.0000 | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0003 | 35.0 | 12040 | 0.0000 | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0003 | 36.0 | 12384 | 0.0000 | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0001 | 37.0 | 12728 | 0.0000 | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0001 | 38.0 | 13072 | 0.0000 | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0001 | 39.0 | 13416 | 0.0000 | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0002 | 40.0 | 13760 | 0.0000 | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0 | 41.0 | 14104 | 0.0000 | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0 | 42.0 | 14448 | 0.0000 | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0 | 43.0 | 14792 | 0.0000 | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0 | 44.0 | 15136 | 0.0000 | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0 | 45.0 | 15480 | 0.0000 | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0 | 46.0 | 15824 | 0.0000 | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0001 | 47.0 | 16168 | 0.0000 | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0 | 48.0 | 16512 | 0.0000 | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0 | 49.0 | 16856 | 0.0000 | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0 | 50.0 | 17200 | 0.0000 | 1.0 | 1.0 | 1.0 | 1.0 | |
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### Framework versions |
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- Transformers 4.30.2 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.13.1 |
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- Tokenizers 0.13.3 |
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