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---
license: apache-2.0
base_model: distilbert/distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- wnut_17
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: my_awesome_wnut_model
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: wnut_17
type: wnut_17
config: wnut_17
split: test
args: wnut_17
metrics:
- name: Precision
type: precision
value: 0.558252427184466
- name: Recall
type: recall
value: 0.4263206672845227
- name: F1
type: f1
value: 0.48344718864950076
- name: Accuracy
type: accuracy
value: 0.9477576845795391
---
<!-- 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. -->
# my_awesome_wnut_model
This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on the wnut_17 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4207
- Precision: 0.5583
- Recall: 0.4263
- F1: 0.4834
- Accuracy: 0.9478
## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 213 | 0.3267 | 0.5351 | 0.4235 | 0.4728 | 0.9472 |
| No log | 2.0 | 426 | 0.3741 | 0.4730 | 0.3818 | 0.4226 | 0.9428 |
| 0.0126 | 3.0 | 639 | 0.3431 | 0.5336 | 0.4189 | 0.4694 | 0.9466 |
| 0.0126 | 4.0 | 852 | 0.3790 | 0.5983 | 0.3920 | 0.4737 | 0.9477 |
| 0.008 | 5.0 | 1065 | 0.3610 | 0.5289 | 0.4328 | 0.4760 | 0.9472 |
| 0.008 | 6.0 | 1278 | 0.3580 | 0.5637 | 0.4347 | 0.4908 | 0.9477 |
| 0.008 | 7.0 | 1491 | 0.3569 | 0.5339 | 0.4458 | 0.4859 | 0.9474 |
| 0.0049 | 8.0 | 1704 | 0.3988 | 0.5602 | 0.4013 | 0.4676 | 0.9470 |
| 0.0049 | 9.0 | 1917 | 0.4180 | 0.5901 | 0.3976 | 0.4751 | 0.9471 |
| 0.0032 | 10.0 | 2130 | 0.3969 | 0.5320 | 0.4161 | 0.4670 | 0.9468 |
| 0.0032 | 11.0 | 2343 | 0.4265 | 0.5851 | 0.4013 | 0.4761 | 0.9473 |
| 0.003 | 12.0 | 2556 | 0.4003 | 0.5569 | 0.4263 | 0.4829 | 0.9475 |
| 0.003 | 13.0 | 2769 | 0.4234 | 0.5936 | 0.3967 | 0.4756 | 0.9480 |
| 0.003 | 14.0 | 2982 | 0.4016 | 0.5482 | 0.4272 | 0.4802 | 0.9482 |
| 0.002 | 15.0 | 3195 | 0.4312 | 0.5655 | 0.4041 | 0.4714 | 0.9471 |
| 0.002 | 16.0 | 3408 | 0.4310 | 0.5611 | 0.4087 | 0.4729 | 0.9470 |
| 0.0014 | 17.0 | 3621 | 0.4287 | 0.5556 | 0.4124 | 0.4734 | 0.9471 |
| 0.0014 | 18.0 | 3834 | 0.4193 | 0.5572 | 0.4198 | 0.4789 | 0.9475 |
| 0.0014 | 19.0 | 4047 | 0.4188 | 0.5583 | 0.4263 | 0.4834 | 0.9478 |
| 0.0014 | 20.0 | 4260 | 0.4207 | 0.5583 | 0.4263 | 0.4834 | 0.9478 |
### Framework versions
- Transformers 4.38.1
- Pytorch 2.1.2
- Datasets 2.1.0
- Tokenizers 0.15.2
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