metadata
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- wnut_17
metrics:
- precision
- recall
- f1
- accuracy
base_model: distilbert-base-uncased
model-index:
- name: token_classification_model
results:
- task:
type: token-classification
name: Token Classification
dataset:
name: wnut_17
type: wnut_17
config: wnut_17
split: test
args: wnut_17
metrics:
- type: precision
value: 0.48380566801619435
name: Precision
- type: recall
value: 0.22150139017608897
name: Recall
- type: f1
value: 0.3038779402415766
name: F1
- type: accuracy
value: 0.936770552776709
name: Accuracy
token_classification_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: 0.2939
- Precision: 0.4838
- Recall: 0.2215
- F1: 0.3039
- Accuracy: 0.9368
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: 32
- eval_batch_size: 32
- 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 | 107 | 0.3091 | 0.4279 | 0.0853 | 0.1422 | 0.9312 |
| No log | 2.0 | 214 | 0.2939 | 0.4838 | 0.2215 | 0.3039 | 0.9368 |
Framework versions
- Transformers 4.28.0
- Pytorch 1.12.1
- Datasets 2.14.4
- Tokenizers 0.13.3