---
license: mit
tags:
- generated_from_trainer
datasets:
- snips_built_in_intents
metrics:
- accuracy
base_model: roberta-base
model-index:
- name: roberta-base-finetuned-intent
  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. -->

# roberta-base-finetuned-intent

This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the snips_built_in_intents dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2720
- Accuracy: 0.9333

## 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: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: IPU
- gradient_accumulation_steps: 8
- total_train_batch_size: 8
- total_eval_batch_size: 5
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- training precision: Mixed Precision

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.9568        | 1.0   | 37   | 1.7598          | 0.4333   |
| 1.2238        | 2.0   | 74   | 0.8130          | 0.7667   |
| 0.4536        | 3.0   | 111  | 0.4985          | 0.8      |
| 0.2478        | 4.0   | 148  | 0.3535          | 0.8667   |
| 0.0903        | 5.0   | 185  | 0.3110          | 0.8667   |
| 0.0849        | 6.0   | 222  | 0.2720          | 0.9333   |
| 0.0708        | 7.0   | 259  | 0.2742          | 0.8667   |
| 0.0796        | 8.0   | 296  | 0.2839          | 0.8667   |
| 0.0638        | 9.0   | 333  | 0.2949          | 0.8667   |
| 0.0566        | 10.0  | 370  | 0.2925          | 0.8667   |


### Framework versions

- Transformers 4.20.1
- Pytorch 1.10.0+cpu
- Datasets 2.7.1
- Tokenizers 0.12.0