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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-cleaned |
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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/bxmx75hr) |
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# deberta-finetuned-ner-microsoft-disaster-cleaned |
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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: 0.0791 |
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- Precision: 0.9251 |
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- Recall: 0.9335 |
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- F1: 0.9292 |
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- Accuracy: 0.9808 |
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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: 4 |
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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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| 0.0908 | 1.0 | 1799 | 0.0765 | 0.9134 | 0.9236 | 0.9185 | 0.9798 | |
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| 0.0668 | 2.0 | 3598 | 0.0735 | 0.9284 | 0.9305 | 0.9295 | 0.9813 | |
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| 0.0515 | 3.0 | 5397 | 0.0756 | 0.9231 | 0.9315 | 0.9273 | 0.9804 | |
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| 0.0394 | 4.0 | 7196 | 0.0791 | 0.9251 | 0.9335 | 0.9292 | 0.9808 | |
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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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