File size: 2,686 Bytes
e495949 9c53c20 e495949 9c53c20 e495949 9c53c20 e495949 9c53c20 e495949 9c53c20 e495949 9c53c20 e495949 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 |
---
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
|