metadata
license: apache-2.0
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: train
args: wnut_17
metrics:
- name: Precision
type: precision
value: 0.5673758865248227
- name: Recall
type: recall
value: 0.2965708989805375
- name: F1
type: f1
value: 0.3895313451004261
- name: Accuracy
type: accuracy
value: 0.9410884528237357
my_awesome_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: 0.2747
- Precision: 0.5674
- Recall: 0.2966
- F1: 0.3895
- Accuracy: 0.9411
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: 2
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 213 | 0.2827 | 0.5605 | 0.2317 | 0.3279 | 0.9380 |
| No log | 2.0 | 426 | 0.2747 | 0.5674 | 0.2966 | 0.3895 | 0.9411 |
Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.7.1
- Tokenizers 0.13.2
Resource: