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---
license: mit
tags:
- generated_from_trainer
datasets:
- clinc_oos
metrics:
- accuracy
model-index:
- name: userutterance_classification_ver1
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: clinc_oos
type: clinc_oos
config: imbalanced
split: validation
args: imbalanced
metrics:
- name: Accuracy
type: accuracy
value: 0.9538709677419355
---
<!-- 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. -->
# userutterance_classification_ver1
This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on the clinc_oos dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2898
- Accuracy: 0.9539
## 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: 4e-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
- lr_scheduler_warmup_steps: 130
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 4.8334 | 0.15 | 200 | 4.7254 | 0.0748 |
| 3.4798 | 0.3 | 400 | 3.4244 | 0.2971 |
| 2.319 | 0.45 | 600 | 2.4423 | 0.5184 |
| 1.5683 | 0.6 | 800 | 1.7401 | 0.6310 |
| 0.9625 | 0.75 | 1000 | 1.2750 | 0.7265 |
| 0.6922 | 0.9 | 1200 | 0.9717 | 0.7761 |
| 0.5019 | 1.05 | 1400 | 0.8036 | 0.8284 |
| 0.3538 | 1.2 | 1600 | 0.6690 | 0.8471 |
| 0.2413 | 1.35 | 1800 | 0.5585 | 0.8713 |
| 0.2623 | 1.5 | 2000 | 0.4840 | 0.8874 |
| 0.2103 | 1.66 | 2200 | 0.4261 | 0.9126 |
| 0.1456 | 1.81 | 2400 | 0.3872 | 0.9152 |
| 0.1276 | 1.96 | 2600 | 0.3329 | 0.9290 |
| 0.09 | 2.11 | 2800 | 0.2925 | 0.9432 |
| 0.0534 | 2.26 | 3000 | 0.2996 | 0.9361 |
| 0.0588 | 2.41 | 3200 | 0.2951 | 0.9403 |
| 0.044 | 2.56 | 3400 | 0.3324 | 0.9403 |
| 0.0535 | 2.71 | 3600 | 0.3155 | 0.9432 |
| 0.0537 | 2.86 | 3800 | 0.3206 | 0.9419 |
| 0.1325 | 3.01 | 4000 | 0.2945 | 0.9465 |
| 0.0611 | 3.16 | 4200 | 0.2903 | 0.9442 |
| 0.0077 | 3.31 | 4400 | 0.3052 | 0.9477 |
| 0.0187 | 3.46 | 4600 | 0.2774 | 0.95 |
| 0.0125 | 3.61 | 4800 | 0.2851 | 0.9513 |
| 0.0157 | 3.76 | 5000 | 0.2883 | 0.9523 |
| 0.0414 | 3.91 | 5200 | 0.3163 | 0.9497 |
| 0.0025 | 4.06 | 5400 | 0.2998 | 0.9494 |
| 0.0019 | 4.21 | 5600 | 0.2925 | 0.9513 |
| 0.0013 | 4.36 | 5800 | 0.2872 | 0.9526 |
| 0.0014 | 4.51 | 6000 | 0.2906 | 0.9532 |
| 0.0015 | 4.67 | 6200 | 0.2862 | 0.9529 |
| 0.0281 | 4.82 | 6400 | 0.2863 | 0.9535 |
| 0.0287 | 4.97 | 6600 | 0.2898 | 0.9539 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
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