---
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
base_model: mmillet/distilrubert-tiny-cased-conversational-v1_single_finetuned_on_cedr_augmented
model-index:
- name: distilrubert_tiny-2nd-finetune-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_tiny-2nd-finetune-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: 0.4467
- Accuracy: 0.8712
- F1: 0.8718
- Precision: 0.8867
- Recall: 0.8712

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.4947        | 1.0   | 12   | 0.4142          | 0.8773   | 0.8777 | 0.8907    | 0.8773 |
| 0.2614        | 2.0   | 24   | 0.3178          | 0.9018   | 0.9011 | 0.9069    | 0.9018 |
| 0.2079        | 3.0   | 36   | 0.3234          | 0.8773   | 0.8784 | 0.8850    | 0.8773 |
| 0.1545        | 4.0   | 48   | 0.3729          | 0.8834   | 0.8830 | 0.8946    | 0.8834 |
| 0.1028        | 5.0   | 60   | 0.2964          | 0.9018   | 0.9016 | 0.9073    | 0.9018 |
| 0.0986        | 6.0   | 72   | 0.2971          | 0.9141   | 0.9139 | 0.9152    | 0.9141 |
| 0.0561        | 7.0   | 84   | 0.3482          | 0.8957   | 0.8962 | 0.9023    | 0.8957 |
| 0.0336        | 8.0   | 96   | 0.3731          | 0.8957   | 0.8953 | 0.9014    | 0.8957 |
| 0.0364        | 9.0   | 108  | 0.4467          | 0.8712   | 0.8718 | 0.8867    | 0.8712 |


### Framework versions

- Transformers 4.20.0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1