---
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