transformers tensorboard safetensors distilbert text-classification generated_from_trainer dataset:imdb 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:
- imdb
license: apache-2.0
metrics:
- accuracy
- f1
- precision
- recall
tags:
- generated_from_trainer
model-index:
- name: distilbert-imdb
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: imdb
      type: imdb
      config: plain_text
      split: test[6250:12500]+test[-12500:-6250]
      args: plain_text
    metrics:
    - type: accuracy
      value: 0.93024
      name: Accuracy
    - type: f1
      value: 0.9302304818748904
      name: F1
    - type: precision
      value: 0.93047490567909
      name: Precision
    - type: recall
      value: 0.93024
      name: Recall
---

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# distilbert-imdb

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2118
- Accuracy: 0.9302
- F1: 0.9302
- Precision: 0.9305
- Recall: 0.9302

## 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: 32
- eval_batch_size: 64
- seed: 9072
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.2803        | 1.0   | 782  | 0.1874          | 0.9276   | 0.9276 | 0.9276    | 0.9276 |
| 0.1143        | 2.0   | 1564 | 0.2118          | 0.9302   | 0.9302 | 0.9305    | 0.9302 |


### Framework versions

- Transformers 4.38.2
- Pytorch 2.1.2
- Datasets 2.1.0
- Tokenizers 0.15.2
