transformers pytorch distilbert text-classification generated_from_trainer dataset:rotten_tomatoes base_model:distilbert/distilbert-base-uncased base_model:finetune:distilbert/distilbert-base-uncased license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
---
base_model: distilbert-base-uncased
datasets:
- rotten_tomatoes
license: apache-2.0
metrics:
- accuracy
- f1
- precision
- recall
tags:
- generated_from_trainer
model-index:
- name: my_distilbert_model
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: rotten_tomatoes
      type: rotten_tomatoes
      config: default
      split: test
      args: default
    metrics:
    - type: accuracy
      value: 0.8470919324577861
      name: Accuracy
    - type: f1
      value: 0.847088568383684
      name: F1
    - type: precision
      value: 0.8471224794412526
      name: Precision
    - type: recall
      value: 0.8470919324577861
      name: Recall
---

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# 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: 0.5418
- Accuracy: 0.8471
- F1: 0.8471
- Precision: 0.8471
- Recall: 0.8471

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.4213        | 1.0   | 534  | 0.3852          | 0.8415   | 0.8411 | 0.8444    | 0.8415 |
| 0.2557        | 2.0   | 1068 | 0.4643          | 0.8377   | 0.8377 | 0.8381    | 0.8377 |
| 0.1571        | 3.0   | 1602 | 0.5418          | 0.8471   | 0.8471 | 0.8471    | 0.8471 |


### Framework versions

- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
