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: 0.5302197802197802
- name: Recall
type: recall
value: 0.3577386468952734
- name: F1
type: f1
value: 0.42722744881018265
- name: Accuracy
type: accuracy
value: 0.945320849899534
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: 0.2636
- Precision: 0.5302
- Recall: 0.3577
- F1: 0.4272
- Accuracy: 0.9453
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 | 0.3099 | 0.3522 | 0.1214 | 0.1806 | 0.9321 |
| No log | 2.0 | 214 | 0.2670 | 0.5139 | 0.3086 | 0.3856 | 0.9410 |
| No log | 3.0 | 321 | 0.2547 | 0.4954 | 0.3466 | 0.4079 | 0.9426 |
| No log | 4.0 | 428 | 0.2553 | 0.5337 | 0.3596 | 0.4297 | 0.9452 |
| 0.1736 | 5.0 | 535 | 0.2636 | 0.5302 | 0.3577 | 0.4272 | 0.9453 |
Framework versions
- Transformers 4.29.2
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3