---
license: mit
base_model: ML-GOD/deberta-finetuned-ner-microsoft-disaster
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/qc6ajeko)
# deberta-finetuned-ner-microsoft-disaster

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.
It achieves the following results on the evaluation set:
- Loss: 0.1757
- Precision: 0.9219
- Recall: 0.9294
- F1: 0.9257
- Accuracy: 0.9797

## 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: 10

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.024         | 1.0   | 1799  | 0.1107          | 0.9149    | 0.9263 | 0.9206 | 0.9790   |
| 0.0201        | 2.0   | 3598  | 0.1197          | 0.9154    | 0.9225 | 0.9190 | 0.9782   |
| 0.014         | 3.0   | 5397  | 0.1249          | 0.9190    | 0.9279 | 0.9235 | 0.9794   |
| 0.0089        | 4.0   | 7196  | 0.1327          | 0.9151    | 0.9221 | 0.9186 | 0.9781   |
| 0.0057        | 5.0   | 8995  | 0.1432          | 0.9117    | 0.9268 | 0.9192 | 0.9789   |
| 0.0049        | 6.0   | 10794 | 0.1610          | 0.9164    | 0.9240 | 0.9202 | 0.9781   |
| 0.0031        | 7.0   | 12593 | 0.1740          | 0.9197    | 0.9273 | 0.9235 | 0.9791   |
| 0.0028        | 8.0   | 14392 | 0.1701          | 0.9222    | 0.9288 | 0.9255 | 0.9797   |
| 0.0022        | 9.0   | 16191 | 0.1750          | 0.9247    | 0.9290 | 0.9268 | 0.9799   |
| 0.0009        | 10.0  | 17990 | 0.1757          | 0.9219    | 0.9294 | 0.9257 | 0.9797   |


### Framework versions

- Transformers 4.42.3
- Pytorch 2.1.2
- Datasets 2.20.0
- Tokenizers 0.19.1