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---
license: mit
base_model: microsoft/deberta-base
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: deberta-finetuned-ner-microsoft-disaster
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<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)
[<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)
# deberta-finetuned-ner-microsoft-disaster
This model is a fine-tuned version of [microsoft/deberta-base](https://huggingface.co/microsoft/deberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1013
- Precision: 0.9216
- Recall: 0.9314
- F1: 0.9265
- Accuracy: 0.9805
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 7
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0872 | 1.0 | 1799 | 0.0762 | 0.9101 | 0.9245 | 0.9172 | 0.9796 |
| 0.0658 | 2.0 | 3598 | 0.0741 | 0.9244 | 0.9288 | 0.9266 | 0.9811 |
| 0.0517 | 3.0 | 5397 | 0.0737 | 0.9282 | 0.9291 | 0.9287 | 0.9808 |
| 0.0383 | 4.0 | 7196 | 0.0834 | 0.9263 | 0.9275 | 0.9269 | 0.9807 |
| 0.0298 | 5.0 | 8995 | 0.0895 | 0.9220 | 0.9299 | 0.9259 | 0.9802 |
| 0.0237 | 6.0 | 10794 | 0.0963 | 0.9203 | 0.9322 | 0.9262 | 0.9806 |
| 0.0182 | 7.0 | 12593 | 0.1013 | 0.9216 | 0.9314 | 0.9265 | 0.9805 |
### Framework versions
- Transformers 4.42.3
- Pytorch 2.1.2
- Datasets 2.20.0
- Tokenizers 0.19.1
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