metadata
license: apache-2.0
base_model: distilbert-base-cased
tags:
- generated_from_trainer
datasets:
- wikiann
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: distilbert-base-cased-wikiann
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: wikiann
type: wikiann
config: en
split: validation
args: en
metrics:
- name: Precision
type: precision
value: 0.7962710012293402
- name: Recall
type: recall
value: 0.8241905839106461
- name: F1
type: f1
value: 0.8099902737251633
- name: Accuracy
type: accuracy
value: 0.926231747293136
distilbert-base-cased-wikiann
This model is a fine-tuned version of distilbert-base-cased on the wikiann dataset. It achieves the following results on the evaluation set:
- Loss: 0.2549
- Precision: 0.7963
- Recall: 0.8242
- F1: 0.8100
- Accuracy: 0.9262
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: 101
- 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 |
|---|---|---|---|---|---|---|---|
| 0.3137 | 1.0 | 1250 | 0.2685 | 0.7716 | 0.8027 | 0.7868 | 0.9181 |
| 0.2199 | 2.0 | 2500 | 0.2526 | 0.7765 | 0.8132 | 0.7944 | 0.9220 |
| 0.1613 | 3.0 | 3750 | 0.2549 | 0.7963 | 0.8242 | 0.8100 | 0.9262 |
Framework versions
- Transformers 4.34.1
- Pytorch 2.0.1+cu117
- Datasets 2.14.6
- Tokenizers 0.14.1