metadata
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
datasets:
- clinc_oos
metrics:
- accuracy
- f1
model-index:
- name: bert-base-uncased-finetuned-clinc_oos
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: clinc_oos
type: clinc_oos
config: plus
split: test
args: plus
metrics:
- name: Accuracy
type: accuracy
value:
accuracy: 0.8672727272727273
- name: F1
type: f1
value:
f1: 0.8593551627139002
bert-base-uncased-finetuned-clinc_oos
This model is a fine-tuned version of bert-base-uncased on the clinc_oos dataset. It achieves the following results on the evaluation set:
- Loss: 1.0863
- Accuracy: {'accuracy': 0.8672727272727273}
- F1: {'f1': 0.8593551627139002}
Model Training Details
| Parameter | Value |
|---|---|
| Task | text-classification |
| Base Model Name | bert-base-uncased |
| Dataset Name | clinc_oos |
| Dataset Config | plus |
| Batch Size | 16 |
| Number of Epochs | 3 |
| Learning Rate | 0.00002 |
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: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| 4.3415 | 1.0 | 954 | 2.4724 | {'accuracy': 0.7769090909090909} | {'f1': 0.7596942777117995} |
| 1.7949 | 2.0 | 1908 | 1.3415 | {'accuracy': 0.8538181818181818} | {'f1': 0.8441232118060242} |
| 0.8898 | 3.0 | 2862 | 1.0863 | {'accuracy': 0.8672727272727273} | {'f1': 0.8593551627139002} |
Framework versions
- Transformers 4.33.3
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3