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---
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: 0.9767741935483871
- task:
type: text-classification
name: Text Classification
dataset:
name: clinc_oos
type: clinc_oos
config: small
split: test
metrics:
- name: Accuracy
type: accuracy
value: 0.9147272727272727
verified: true
- name: Precision Macro
type: precision
value: 0.9134409304951565
verified: true
- name: Precision Micro
type: precision
value: 0.9147272727272727
verified: true
- name: Precision Weighted
type: precision
value: 0.9265787347428621
verified: true
- name: Recall Macro
type: recall
value: 0.9703421633554087
verified: true
- name: Recall Micro
type: recall
value: 0.9147272727272727
verified: true
- name: Recall Weighted
type: recall
value: 0.9147272727272727
verified: true
- name: F1 Macro
type: f1
value: 0.9383259127958784
verified: true
- name: F1 Micro
type: f1
value: 0.9147272727272727
verified: true
- name: F1 Weighted
type: f1
value: 0.9117668013116867
verified: true
- name: loss
type: loss
value: 0.4619016647338867
verified: true
---
<!-- 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: 0.1545
- Accuracy: 0.9768
## 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
- distributed_type: sagemaker_data_parallel
- num_devices: 8
- total_train_batch_size: 128
- total_eval_batch_size: 128
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 5.0548 | 1.0 | 120 | 5.0359 | 0.0071 |
| 4.4725 | 2.0 | 240 | 2.9385 | 0.7558 |
| 1.8924 | 3.0 | 360 | 0.6456 | 0.9374 |
| 0.4552 | 4.0 | 480 | 0.2297 | 0.9626 |
| 0.1589 | 5.0 | 600 | 0.1545 | 0.9768 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.2+cu113
- Datasets 1.18.4
- Tokenizers 0.11.6
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