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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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- wnut_17 |
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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: ner_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: wnut_17 |
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type: wnut_17 |
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config: wnut_17 |
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split: test |
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args: wnut_17 |
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metrics: |
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- name: Precision |
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type: precision |
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value: 0.5632040050062578 |
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- name: Recall |
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type: recall |
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value: 0.4170528266913809 |
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- name: F1 |
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type: f1 |
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value: 0.47923322683706066 |
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- name: Accuracy |
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type: accuracy |
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value: 0.9478859390363815 |
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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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# ner_model |
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This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the wnut_17 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3832 |
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- Precision: 0.5632 |
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- Recall: 0.4171 |
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- F1: 0.4792 |
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- Accuracy: 0.9479 |
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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: 10 |
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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 | 425 | 0.2828 | 0.6021 | 0.3800 | 0.4659 | 0.9466 | |
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| 0.074 | 2.0 | 850 | 0.2955 | 0.5825 | 0.3892 | 0.4667 | 0.9474 | |
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| 0.0457 | 3.0 | 1275 | 0.3072 | 0.5857 | 0.4180 | 0.4878 | 0.9492 | |
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| 0.0234 | 4.0 | 1700 | 0.3430 | 0.5911 | 0.4059 | 0.4813 | 0.9481 | |
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| 0.0144 | 5.0 | 2125 | 0.3468 | 0.5406 | 0.4198 | 0.4726 | 0.9476 | |
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| 0.0107 | 6.0 | 2550 | 0.3742 | 0.5541 | 0.4032 | 0.4667 | 0.9470 | |
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| 0.0107 | 7.0 | 2975 | 0.3779 | 0.5861 | 0.4133 | 0.4848 | 0.9483 | |
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| 0.0081 | 8.0 | 3400 | 0.3802 | 0.5537 | 0.4013 | 0.4653 | 0.9477 | |
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| 0.0059 | 9.0 | 3825 | 0.3750 | 0.5511 | 0.4198 | 0.4766 | 0.9478 | |
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| 0.0033 | 10.0 | 4250 | 0.3832 | 0.5632 | 0.4171 | 0.4792 | 0.9479 | |
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### Framework versions |
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- Transformers 4.41.2 |
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- Pytorch 2.3.0+cu121 |
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- Datasets 2.19.2 |
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- Tokenizers 0.19.1 |
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