---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- rotten_tomatoes
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: my_distilbert_model
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: rotten_tomatoes
      type: rotten_tomatoes
      config: default
      split: test
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.849906191369606
    - name: F1
      type: f1
      value: 0.8499040780048225
    - name: Precision
      type: precision
      value: 0.8499258993286938
    - name: Recall
      type: recall
      value: 0.849906191369606
---

<!-- 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. -->

# my_distilbert_model

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the rotten_tomatoes dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5344
- Accuracy: 0.8499
- F1: 0.8499
- Precision: 0.8499
- Recall: 0.8499

## 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: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.4179        | 1.0   | 534  | 0.3769          | 0.8415   | 0.8413 | 0.8428    | 0.8415 |
| 0.2395        | 2.0   | 1068 | 0.4314          | 0.8490   | 0.8490 | 0.8490    | 0.8490 |
| 0.1638        | 3.0   | 1602 | 0.5344          | 0.8499   | 0.8499 | 0.8499    | 0.8499 |


### Framework versions

- Transformers 4.33.2
- Pytorch 1.10.0
- Datasets 2.14.5
- Tokenizers 0.13.3