---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: distil_bert_own_txt_clf_model
  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. -->

# distil_bert_own_txt_clf_model

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: 1.0454
- Accuracy: 0.8167
- F1: 0.8125
- Precision: 0.8152
- Recall: 0.8127

## 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: 5e-05
- train_batch_size: 16
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 50
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 1.3554        | 3.33  | 50   | 1.1341          | 0.5333   | 0.3913 | 0.5676    | 0.4930 |
| 1.0538        | 6.67  | 100  | 1.1938          | 0.5833   | 0.5168 | 0.5381    | 0.5736 |
| 0.3226        | 10.0  | 150  | 1.1243          | 0.6833   | 0.6412 | 0.7731    | 0.6614 |
| 0.0674        | 13.33 | 200  | 0.8542          | 0.8417   | 0.8317 | 0.8442    | 0.8405 |
| 0.0022        | 16.67 | 250  | 0.7832          | 0.8333   | 0.8252 | 0.8323    | 0.8277 |
| 0.001         | 20.0  | 300  | 0.8204          | 0.8333   | 0.8265 | 0.8312    | 0.8307 |
| 0.0005        | 23.33 | 350  | 0.8259          | 0.8583   | 0.8537 | 0.8534    | 0.8589 |
| 0.0005        | 26.67 | 400  | 0.8121          | 0.825    | 0.8165 | 0.8230    | 0.8134 |
| 0.0004        | 30.0  | 450  | 0.8673          | 0.8583   | 0.8537 | 0.8534    | 0.8589 |
| 0.0003        | 33.33 | 500  | 0.8052          | 0.8583   | 0.8543 | 0.8561    | 0.8538 |
| 0.0003        | 36.67 | 550  | 0.8324          | 0.8417   | 0.8363 | 0.8392    | 0.8372 |
| 0.0003        | 40.0  | 600  | 0.8536          | 0.85     | 0.8445 | 0.8450    | 0.8480 |
| 0.0003        | 43.33 | 650  | 0.8632          | 0.85     | 0.8445 | 0.8450    | 0.8480 |
| 0.0003        | 46.67 | 700  | 0.8690          | 0.85     | 0.8445 | 0.8450    | 0.8480 |
| 0.0003        | 50.0  | 750  | 0.8715          | 0.85     | 0.8445 | 0.8450    | 0.8480 |


### Framework versions

- Transformers 4.38.1
- Pytorch 2.1.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2