---
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: distilrubert-tiny-cased-conversational-v1_single_finetuned_on_cedr_augmented
  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-cased-conversational-v1_single_finetuned_on_cedr_augmented

This model is a fine-tuned version of [DeepPavlov/distilrubert-tiny-cased-conversational-v1](https://huggingface.co/DeepPavlov/distilrubert-tiny-cased-conversational-v1) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5908
- Accuracy: 0.8653
- F1: 0.8656
- Precision: 0.8665
- Recall: 0.8653

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.9172        | 1.0   | 69   | 0.5124          | 0.8246   | 0.8220 | 0.8271    | 0.8246 |
| 0.4709        | 2.0   | 138  | 0.4279          | 0.8528   | 0.8505 | 0.8588    | 0.8528 |
| 0.3194        | 3.0   | 207  | 0.3770          | 0.8737   | 0.8727 | 0.8740    | 0.8737 |
| 0.2459        | 4.0   | 276  | 0.3951          | 0.8685   | 0.8682 | 0.8692    | 0.8685 |
| 0.1824        | 5.0   | 345  | 0.4005          | 0.8831   | 0.8834 | 0.8841    | 0.8831 |
| 0.1515        | 6.0   | 414  | 0.4356          | 0.8800   | 0.8797 | 0.8801    | 0.8800 |
| 0.1274        | 7.0   | 483  | 0.4642          | 0.8727   | 0.8726 | 0.8731    | 0.8727 |
| 0.0833        | 8.0   | 552  | 0.5226          | 0.8633   | 0.8627 | 0.8631    | 0.8633 |
| 0.073         | 9.0   | 621  | 0.5327          | 0.8695   | 0.8686 | 0.8692    | 0.8695 |
| 0.0575        | 10.0  | 690  | 0.5908          | 0.8653   | 0.8656 | 0.8665    | 0.8653 |


### Framework versions

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