metadata
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- wnut_17
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner-wnut17
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: wnut_17
type: wnut_17
config: wnut_17
split: train
args: wnut_17
metrics:
- name: Precision
type: precision
value: 0.5301047120418848
- name: Recall
type: recall
value: 0.48444976076555024
- name: F1
type: f1
value: 0.50625
- name: Accuracy
type: accuracy
value: 0.9252876639015253
bert-finetuned-ner-wnut17
This model is a fine-tuned version of bert-base-cased on the wnut_17 dataset. It achieves the following results on the evaluation set:
- Loss: 0.3444
- Precision: 0.5301
- Recall: 0.4844
- F1: 0.5062
- Accuracy: 0.9253
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 | 0.3361 | 0.5602 | 0.4007 | 0.4672 | 0.9172 |
0.2009 | 2.0 | 850 | 0.3617 | 0.5331 | 0.4043 | 0.4599 | 0.9201 |
0.0947 | 3.0 | 1275 | 0.3444 | 0.5301 | 0.4844 | 0.5062 | 0.9253 |
Framework versions
- Transformers 4.21.1
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1