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---
license: apache-2.0
base_model: 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: 0.5524652338811631
- name: Recall
type: recall
value: 0.40500463392029656
- name: F1
type: f1
value: 0.467379679144385
- name: Accuracy
type: accuracy
value: 0.9464751400111154
---
<!-- 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-base-uncased](https://huggingface.co/distilbert-base-uncased) on the wnut_17 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4068
- Precision: 0.5525
- Recall: 0.4050
- F1: 0.4674
- Accuracy: 0.9465
## 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 | 0.2742 | 0.5216 | 0.3355 | 0.4083 | 0.9421 |
| No log | 2.0 | 426 | 0.2810 | 0.6107 | 0.3503 | 0.4452 | 0.9455 |
| 0.0744 | 3.0 | 639 | 0.3305 | 0.6560 | 0.3411 | 0.4488 | 0.9456 |
| 0.0744 | 4.0 | 852 | 0.3382 | 0.5480 | 0.3596 | 0.4342 | 0.9443 |
| 0.0295 | 5.0 | 1065 | 0.3461 | 0.5635 | 0.3865 | 0.4585 | 0.9454 |
| 0.0295 | 6.0 | 1278 | 0.3823 | 0.5744 | 0.3828 | 0.4594 | 0.9454 |
| 0.0295 | 7.0 | 1491 | 0.3404 | 0.5080 | 0.4096 | 0.4536 | 0.9445 |
| 0.0128 | 8.0 | 1704 | 0.3926 | 0.5302 | 0.3744 | 0.4389 | 0.9441 |
| 0.0128 | 9.0 | 1917 | 0.3505 | 0.5033 | 0.4226 | 0.4594 | 0.9449 |
| 0.0071 | 10.0 | 2130 | 0.3825 | 0.5685 | 0.3846 | 0.4588 | 0.9456 |
| 0.0071 | 11.0 | 2343 | 0.3806 | 0.5155 | 0.4171 | 0.4611 | 0.9451 |
| 0.0044 | 12.0 | 2556 | 0.4035 | 0.5422 | 0.3985 | 0.4594 | 0.9454 |
| 0.0044 | 13.0 | 2769 | 0.4106 | 0.5940 | 0.3865 | 0.4683 | 0.9465 |
| 0.0044 | 14.0 | 2982 | 0.4069 | 0.5485 | 0.4032 | 0.4647 | 0.9457 |
| 0.0032 | 15.0 | 3195 | 0.4280 | 0.6029 | 0.3800 | 0.4662 | 0.9466 |
| 0.0032 | 16.0 | 3408 | 0.4049 | 0.5798 | 0.4208 | 0.4876 | 0.9472 |
| 0.0026 | 17.0 | 3621 | 0.4129 | 0.5758 | 0.4013 | 0.4730 | 0.9470 |
| 0.0026 | 18.0 | 3834 | 0.4131 | 0.5731 | 0.4069 | 0.4759 | 0.9469 |
| 0.0021 | 19.0 | 4047 | 0.4074 | 0.5557 | 0.4022 | 0.4667 | 0.9465 |
| 0.0021 | 20.0 | 4260 | 0.4068 | 0.5525 | 0.4050 | 0.4674 | 0.9465 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
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