metadata
license: mit
base_model: dslim/bert-base-NER
tags:
- generated_from_trainer
datasets:
- wnut_17
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: my_finetuned_wnut_model_1012
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: 0.5479274611398963
- name: Recall
type: recall
value: 0.39202965708989806
- name: F1
type: f1
value: 0.45705024311183146
- name: Accuracy
type: accuracy
value: 0.9487047961015646
my_finetuned_wnut_model_1012
This model is a fine-tuned version of dslim/bert-base-NER on the wnut_17 dataset. It achieves the following results on the evaluation set:
- Loss: 0.2940
- Precision: 0.5479
- Recall: 0.3920
- F1: 0.4571
- Accuracy: 0.9487
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: 2
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 213 | 0.2657 | 0.5157 | 0.3967 | 0.4484 | 0.9468 |
| No log | 2.0 | 426 | 0.2940 | 0.5479 | 0.3920 | 0.4571 | 0.9487 |
Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1