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--- |
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license: mit |
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base_model: microsoft/deberta-base |
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tags: |
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- generated_from_trainer |
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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: deberta-finetuned-ner-microsoft-disaster |
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results: [] |
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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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[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/akku/huggingface/runs/bxwrwawl) |
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[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/akku/huggingface/runs/bxwrwawl) |
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# deberta-finetuned-ner-microsoft-disaster |
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This model is a fine-tuned version of [microsoft/deberta-base](https://huggingface.co/microsoft/deberta-base) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1013 |
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- Precision: 0.9216 |
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- Recall: 0.9314 |
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- F1: 0.9265 |
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- Accuracy: 0.9805 |
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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: 7 |
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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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| 0.0872 | 1.0 | 1799 | 0.0762 | 0.9101 | 0.9245 | 0.9172 | 0.9796 | |
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| 0.0658 | 2.0 | 3598 | 0.0741 | 0.9244 | 0.9288 | 0.9266 | 0.9811 | |
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| 0.0517 | 3.0 | 5397 | 0.0737 | 0.9282 | 0.9291 | 0.9287 | 0.9808 | |
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| 0.0383 | 4.0 | 7196 | 0.0834 | 0.9263 | 0.9275 | 0.9269 | 0.9807 | |
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| 0.0298 | 5.0 | 8995 | 0.0895 | 0.9220 | 0.9299 | 0.9259 | 0.9802 | |
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| 0.0237 | 6.0 | 10794 | 0.0963 | 0.9203 | 0.9322 | 0.9262 | 0.9806 | |
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| 0.0182 | 7.0 | 12593 | 0.1013 | 0.9216 | 0.9314 | 0.9265 | 0.9805 | |
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### Framework versions |
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- Transformers 4.42.3 |
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- Pytorch 2.1.2 |
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- Datasets 2.20.0 |
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- Tokenizers 0.19.1 |
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