metadata
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- wnut_17
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-small-finetuned-xglue-ner-longer10
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: 0.5436746987951807
- name: Recall
type: recall
value: 0.4318181818181818
- name: F1
type: f1
value: 0.48133333333333334
- name: Accuracy
type: accuracy
value: 0.9250487441220323
bert-small-finetuned-xglue-ner-longer10
This model is a fine-tuned version of muhtasham/bert-small-finetuned-xglue-ner-longer6 on the wnut_17 dataset. It achieves the following results on the evaluation set:
- Loss: 0.4645
- Precision: 0.5437
- Recall: 0.4318
- F1: 0.4813
- Accuracy: 0.9250
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: 4
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 425 | 0.4872 | 0.6164 | 0.3959 | 0.4822 | 0.9253 |
| 0.0385 | 2.0 | 850 | 0.4528 | 0.5512 | 0.4246 | 0.4797 | 0.9256 |
| 0.0317 | 3.0 | 1275 | 0.4638 | 0.5431 | 0.4294 | 0.4796 | 0.9246 |
| 0.0308 | 4.0 | 1700 | 0.4645 | 0.5437 | 0.4318 | 0.4813 | 0.9250 |
Framework versions
- Transformers 4.21.1
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1