metadata
tags:
- generated_from_trainer
datasets:
- wnut_17
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: wnut_17
type: wnut_17
config: wnut_17
split: validation
args: wnut_17
metrics:
- name: Precision
type: precision
value: 0.6274509803921569
- name: Recall
type: recall
value: 0.49760765550239233
- name: F1
type: f1
value: 0.5550366911274184
- name: Accuracy
type: accuracy
value: 0.9333784769246797
bert-finetuned-ner
This model was trained from scratch on the wnut_17 dataset. It achieves the following results on the evaluation set:
- Loss: 0.4590
- Precision: 0.6275
- Recall: 0.4976
- F1: 0.5550
- Accuracy: 0.9334
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: 8
- eval_batch_size: 8
- seed: 42
- 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 |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 425 | 0.4576 | 0.6556 | 0.4713 | 0.5484 | 0.9321 |
| 0.0403 | 2.0 | 850 | 0.4647 | 0.6293 | 0.4629 | 0.5334 | 0.9311 |
| 0.0227 | 3.0 | 1275 | 0.4590 | 0.6275 | 0.4976 | 0.5550 | 0.9334 |
Framework versions
- Transformers 4.27.0.dev0
- Pytorch 2.0.1
- Datasets 2.12.0
- Tokenizers 0.13.2