---
tags:
- generated_from_trainer
datasets:
- clinc_oos
metrics:
- accuracy
model-index:
- name: MiniLMv2-L12-H384-distilled-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: 0.9529032258064516
---

<!-- 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. -->

# MiniLMv2-L12-H384-distilled-finetuned-clinc

This model is a fine-tuned version of [nreimers/MiniLMv2-L12-H384-distilled-from-RoBERTa-Large](https://huggingface.co/nreimers/MiniLMv2-L12-H384-distilled-from-RoBERTa-Large) on the clinc_oos dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3058
- Accuracy: 0.9529

## 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: 0.0001
- train_batch_size: 64
- eval_batch_size: 64
- seed: 33
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.9908        | 1.0   | 239  | 1.6816          | 0.3910   |
| 1.5212        | 2.0   | 478  | 1.2365          | 0.7697   |
| 1.129         | 3.0   | 717  | 0.9209          | 0.8706   |
| 0.8462        | 4.0   | 956  | 0.6978          | 0.9152   |
| 0.6497        | 5.0   | 1195 | 0.5499          | 0.9342   |
| 0.5124        | 6.0   | 1434 | 0.4447          | 0.9445   |
| 0.4196        | 7.0   | 1673 | 0.3797          | 0.9455   |
| 0.3587        | 8.0   | 1912 | 0.3358          | 0.95     |
| 0.3228        | 9.0   | 2151 | 0.3133          | 0.9513   |
| 0.3052        | 10.0  | 2390 | 0.3058          | 0.9529   |


### Framework versions

- Transformers 4.17.0
- Pytorch 1.10.2+cu113
- Datasets 1.18.4
- Tokenizers 0.11.6