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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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- szeged_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: hun_wnut_modell |
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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: szeged_ner |
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type: szeged_ner |
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config: business |
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split: test |
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args: business |
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metrics: |
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- name: Precision |
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type: precision |
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value: 0.8590342679127726 |
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- name: Recall |
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type: recall |
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value: 0.9004081632653061 |
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- name: F1 |
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type: f1 |
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value: 0.8792347548824233 |
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- name: Accuracy |
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type: accuracy |
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value: 0.9881996563884619 |
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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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# hun_wnut_modell |
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This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the szeged_ner dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0419 |
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- Precision: 0.8590 |
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- Recall: 0.9004 |
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- F1: 0.8792 |
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- Accuracy: 0.9882 |
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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: 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: 5 |
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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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| 0.2035 | 1.0 | 511 | 0.0665 | 0.8124 | 0.8343 | 0.8232 | 0.9813 | |
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| 0.075 | 2.0 | 1022 | 0.0501 | 0.8280 | 0.8841 | 0.8551 | 0.9847 | |
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| 0.0498 | 3.0 | 1533 | 0.0444 | 0.8452 | 0.8914 | 0.8677 | 0.9866 | |
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| 0.0354 | 4.0 | 2044 | 0.0417 | 0.8661 | 0.8980 | 0.8818 | 0.9885 | |
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| 0.0275 | 5.0 | 2555 | 0.0419 | 0.8590 | 0.9004 | 0.8792 | 0.9882 | |
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### Framework versions |
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- Transformers 4.32.0 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.14.4 |
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- Tokenizers 0.13.3 |
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