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--- |
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license: mit |
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base_model: ML-GOD/deberta-finetuned-ner-microsoft-disaster |
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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/qc6ajeko) |
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# deberta-finetuned-ner-microsoft-disaster |
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This model is a fine-tuned version of [ML-GOD/deberta-finetuned-ner-microsoft-disaster](https://huggingface.co/ML-GOD/deberta-finetuned-ner-microsoft-disaster) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1757 |
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- Precision: 0.9219 |
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- Recall: 0.9294 |
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- F1: 0.9257 |
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- Accuracy: 0.9797 |
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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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| 0.024 | 1.0 | 1799 | 0.1107 | 0.9149 | 0.9263 | 0.9206 | 0.9790 | |
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| 0.0201 | 2.0 | 3598 | 0.1197 | 0.9154 | 0.9225 | 0.9190 | 0.9782 | |
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| 0.014 | 3.0 | 5397 | 0.1249 | 0.9190 | 0.9279 | 0.9235 | 0.9794 | |
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| 0.0089 | 4.0 | 7196 | 0.1327 | 0.9151 | 0.9221 | 0.9186 | 0.9781 | |
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| 0.0057 | 5.0 | 8995 | 0.1432 | 0.9117 | 0.9268 | 0.9192 | 0.9789 | |
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| 0.0049 | 6.0 | 10794 | 0.1610 | 0.9164 | 0.9240 | 0.9202 | 0.9781 | |
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| 0.0031 | 7.0 | 12593 | 0.1740 | 0.9197 | 0.9273 | 0.9235 | 0.9791 | |
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| 0.0028 | 8.0 | 14392 | 0.1701 | 0.9222 | 0.9288 | 0.9255 | 0.9797 | |
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| 0.0022 | 9.0 | 16191 | 0.1750 | 0.9247 | 0.9290 | 0.9268 | 0.9799 | |
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| 0.0009 | 10.0 | 17990 | 0.1757 | 0.9219 | 0.9294 | 0.9257 | 0.9797 | |
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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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