metadata
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- wnut_17
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: ner-classification
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: wnut_17
type: wnut_17
args: wnut_17
metrics:
- name: Precision
type: precision
value: 0.5421686746987951
- name: Recall
type: recall
value: 0.3336422613531047
- name: F1
type: f1
value: 0.41308089500860584
- name: Accuracy
type: accuracy
value: 0.9439100508742679
ner-classification
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: 0.2699
- Precision: 0.5422
- Recall: 0.3336
- F1: 0.4131
- Accuracy: 0.9439
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: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 213 | 0.2797 | 0.5105 | 0.2484 | 0.3342 | 0.9386 |
| No log | 2.0 | 426 | 0.2636 | 0.5493 | 0.3151 | 0.4005 | 0.9430 |
| 0.1938 | 3.0 | 639 | 0.2699 | 0.5422 | 0.3336 | 0.4131 | 0.9439 |
Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0+cu102
- Datasets 2.4.0
- Tokenizers 0.12.1