|
--- |
|
license: mit |
|
tags: |
|
- generated_from_trainer |
|
datasets: |
|
- clinc_oos |
|
metrics: |
|
- accuracy |
|
model-index: |
|
- name: roberta-large-finetuned-clinc |
|
results: |
|
- task: |
|
name: Text Classification |
|
type: text-classification |
|
dataset: |
|
name: clinc_oos |
|
type: clinc_oos |
|
args: plus |
|
metrics: |
|
- name: Accuracy |
|
type: accuracy |
|
value: 0.9703225806451613 |
|
--- |
|
|
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
|
should probably proofread and complete it, then remove this comment. --> |
|
|
|
# roberta-large-finetuned-clinc |
|
|
|
This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the clinc_oos dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.2109 |
|
- Accuracy: 0.9703 |
|
|
|
## 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: 128 |
|
- eval_batch_size: 128 |
|
- seed: 42 |
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
- lr_scheduler_type: linear |
|
- lr_scheduler_warmup_steps: 500 |
|
- num_epochs: 5 |
|
- mixed_precision_training: Native AMP |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
|
|:-------------:|:-----:|:----:|:---------------:|:--------:| |
|
| 5.0643 | 1.0 | 120 | 5.0440 | 0.0065 | |
|
| 4.2726 | 2.0 | 240 | 2.7488 | 0.7255 | |
|
| 1.9687 | 3.0 | 360 | 0.8694 | 0.9174 | |
|
| 0.5773 | 4.0 | 480 | 0.3267 | 0.9539 | |
|
| 0.1842 | 5.0 | 600 | 0.2109 | 0.9703 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.19.0.dev0 |
|
- Pytorch 1.10.2+cu113 |
|
- Datasets 1.18.4 |
|
- Tokenizers 0.11.6 |
|
|
