metadata
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- wnut_17
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: ner
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: 0.5552523874488404
- name: Recall
type: recall
value: 0.37720111214087115
- name: F1
type: f1
value: 0.44922737306843263
- name: Accuracy
type: accuracy
value: 0.9469454063528707
ner
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: 0.2942
- Precision: 0.5553
- Recall: 0.3772
- F1: 0.4492
- Accuracy: 0.9469
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: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 213 | 0.2666 | 0.6024 | 0.2808 | 0.3831 | 0.9405 |
| No log | 2.0 | 426 | 0.2605 | 0.5708 | 0.3364 | 0.4233 | 0.9456 |
| 0.1299 | 3.0 | 639 | 0.2827 | 0.5658 | 0.3346 | 0.4205 | 0.9452 |
| 0.1299 | 4.0 | 852 | 0.2836 | 0.5503 | 0.3753 | 0.4463 | 0.9469 |
| 0.051 | 5.0 | 1065 | 0.2942 | 0.5553 | 0.3772 | 0.4492 | 0.9469 |
Framework versions
- Transformers 4.27.4
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3