LABEL_0 LABEL_1 LABEL_2 LABEL_3
---
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: distilrubert-2ndfinetune-epru
  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. -->

# distilrubert-2ndfinetune-epru

This model is a fine-tuned version of [mmillet/distilrubert-tiny-cased-conversational-v1_best_finetuned_emotion_experiment_augmented_anger_fear](https://huggingface.co/mmillet/distilrubert-tiny-cased-conversational-v1_best_finetuned_emotion_experiment_augmented_anger_fear) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3531
- Accuracy: 0.9054
- F1: 0.9034
- Precision: 0.9074
- Recall: 0.9054

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.4716        | 1.0   | 11   | 0.2851          | 0.8986   | 0.8945 | 0.9029    | 0.8986 |
| 0.2842        | 2.0   | 22   | 0.3041          | 0.8851   | 0.8796 | 0.8816    | 0.8851 |
| 0.167         | 3.0   | 33   | 0.2996          | 0.8986   | 0.8914 | 0.8997    | 0.8986 |
| 0.1527        | 4.0   | 44   | 0.2443          | 0.9189   | 0.9163 | 0.9222    | 0.9189 |
| 0.0926        | 5.0   | 55   | 0.2777          | 0.9054   | 0.9016 | 0.9059    | 0.9054 |
| 0.0897        | 6.0   | 66   | 0.3081          | 0.9122   | 0.9080 | 0.9147    | 0.9122 |
| 0.0438        | 7.0   | 77   | 0.3332          | 0.8986   | 0.8952 | 0.8993    | 0.8986 |
| 0.0433        | 8.0   | 88   | 0.3480          | 0.8851   | 0.8859 | 0.8896    | 0.8851 |
| 0.0398        | 9.0   | 99   | 0.3531          | 0.9054   | 0.9034 | 0.9074    | 0.9054 |


### Framework versions

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