metadata
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- wnut_17
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: distilbert-base-token
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: 0.5348837209302325
- name: Recall
type: recall
value: 0.3410565338276182
- name: F1
type: f1
value: 0.4165251839275608
- name: Accuracy
type: accuracy
value: 0.944636826129708
distilbert-base-token
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: 0.2714
- Precision: 0.5349
- Recall: 0.3411
- F1: 0.4165
- Accuracy: 0.9446
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: 5
Training results
Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
---|---|---|---|---|---|---|---|
No log | 1.0 | 107 | 0.3073 | 0.3474 | 0.1223 | 0.1809 | 0.9325 |
No log | 2.0 | 214 | 0.2605 | 0.4597 | 0.2956 | 0.3598 | 0.9409 |
No log | 3.0 | 321 | 0.2563 | 0.5319 | 0.3318 | 0.4087 | 0.9431 |
No log | 4.0 | 428 | 0.2581 | 0.5255 | 0.3531 | 0.4224 | 0.9449 |
0.1669 | 5.0 | 535 | 0.2714 | 0.5349 | 0.3411 | 0.4165 | 0.9446 |
Framework versions
- Transformers 4.29.2
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3