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---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: domain_classification
results: []
---
<!-- 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. -->
# Domain classification model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the Opus dataset with IT, medical, law domain.They are extracted from OPUS dataset in english.
It achieves the following results on the evaluation set:
- Loss: 0.1140
- Accuracy: 0.9750
## Model description
The model will classify sentence which belongs to its domain.
## 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: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.13 | 1.0 | 2838 | 0.1201 | 0.9691 |
| 0.0609 | 2.0 | 5676 | 0.1140 | 0.9750 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
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