---
library_name: transformers
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-distilled-clinc2
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# distilbert-base-uncased-distilled-clinc2

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2271
- Accuracy: 0.9565

## 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: 0.0002134538968230803
- train_batch_size: 192
- eval_batch_size: 48
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1.7007007109718952e-07
- lr_scheduler_type: linear
- num_epochs: 13

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 80   | 0.4718          | 0.8939   |
| No log        | 2.0   | 160  | 0.2975          | 0.9361   |
| No log        | 3.0   | 240  | 0.2616          | 0.9494   |
| 0.5682        | 4.0   | 320  | 0.2531          | 0.9465   |
| 0.5682        | 5.0   | 400  | 0.2412          | 0.9523   |
| 0.5682        | 6.0   | 480  | 0.2452          | 0.9474   |
| 0.5682        | 7.0   | 560  | 0.2388          | 0.9503   |
| 0.1756        | 8.0   | 640  | 0.2342          | 0.9523   |
| 0.1756        | 9.0   | 720  | 0.2289          | 0.9542   |
| 0.1756        | 10.0  | 800  | 0.2290          | 0.9545   |
| 0.1756        | 11.0  | 880  | 0.2261          | 0.9558   |
| 0.1648        | 12.0  | 960  | 0.2267          | 0.9558   |
| 0.1648        | 13.0  | 1040 | 0.2271          | 0.9565   |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.2.2+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1