metadata
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- wnut_17
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: test_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: 0.516566265060241
- name: Recall
type: recall
value: 0.3178869323447637
- name: F1
type: f1
value: 0.39357429718875503
- name: Accuracy
type: accuracy
value: 0.9431405241332136
test_wnut_model
This model is a fine-tuned version of distilbert-base-uncased on the wnut_17 dataset. It achieves the following results on the evaluation set:
- Loss: 0.2848
- Precision: 0.5166
- Recall: 0.3179
- F1: 0.3936
- Accuracy: 0.9431
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: 5e-06
- train_batch_size: 6
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
Training results
Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
---|---|---|---|---|---|---|---|
0.376 | 1.0 | 566 | 0.3069 | 0.3829 | 0.1242 | 0.1875 | 0.9331 |
0.1664 | 2.0 | 1132 | 0.2941 | 0.5151 | 0.2530 | 0.3393 | 0.9387 |
0.1259 | 3.0 | 1698 | 0.3256 | 0.5982 | 0.2456 | 0.3482 | 0.9405 |
0.1189 | 4.0 | 2264 | 0.2935 | 0.5420 | 0.3049 | 0.3903 | 0.9428 |
0.0992 | 5.0 | 2830 | 0.2848 | 0.5166 | 0.3179 | 0.3936 | 0.9431 |
Framework versions
- Transformers 4.31.0
- Pytorch 2.0.0
- Datasets 2.14.2
- Tokenizers 0.13.3