LABEL_0 LABEL_1 LABEL_2 LABEL_3
---
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: distilrubert-tiny-2ndfinetune-epru
  results: []
---

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# distilrubert-tiny-2ndfinetune-epru

This model is a fine-tuned version of [mmillet/distilrubert-tiny-cased-conversational-v1_single_finetuned_on_cedr_augmented](https://huggingface.co/mmillet/distilrubert-tiny-cased-conversational-v1_single_finetuned_on_cedr_augmented) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2085
- Accuracy: 0.9333
- F1: 0.9319
- Precision: 0.9336
- Recall: 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: 0.0001
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-06
- lr_scheduler_type: linear
- num_epochs: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.4825        | 1.0   | 13   | 0.2988          | 0.8848   | 0.8827 | 0.9056    | 0.8848 |
| 0.2652        | 2.0   | 26   | 0.2435          | 0.9212   | 0.9216 | 0.9282    | 0.9212 |
| 0.168         | 3.0   | 39   | 0.2120          | 0.9515   | 0.9501 | 0.9524    | 0.9515 |
| 0.1593        | 4.0   | 52   | 0.1962          | 0.9333   | 0.9330 | 0.9366    | 0.9333 |
| 0.1294        | 5.0   | 65   | 0.1855          | 0.9333   | 0.9334 | 0.9355    | 0.9333 |
| 0.1065        | 6.0   | 78   | 0.1780          | 0.9394   | 0.9393 | 0.9399    | 0.9394 |
| 0.0908        | 7.0   | 91   | 0.1967          | 0.9394   | 0.9388 | 0.9388    | 0.9394 |
| 0.0432        | 8.0   | 104  | 0.2085          | 0.9333   | 0.9319 | 0.9336    | 0.9333 |


### Framework versions

- Transformers 4.19.3
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
