transformers pytorch distilbert text-classification generated_from_trainer dataset:imdb license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
---
datasets:
- imdb
license: apache-2.0
metrics:
- accuracy
- f1
- precision
- recall
tags:
- generated_from_trainer
model-index:
- name: r-distilbert-base-uncased-otel
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: imdb
      type: imdb
      config: plain_text
      split: test
      args: plain_text
    metrics:
    - type: accuracy
      value: 0.9352
      name: Accuracy
    - type: f1
      value: 0.93575252825699
      name: F1
    - type: precision
      value: 0.9300354749704375
      name: Precision
    - type: recall
      value: 0.9415403032721469
      name: Recall
---

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# r-distilbert-base-uncased-otel

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: 0.2594
- Accuracy: 0.9352
- F1: 0.9358
- Precision: 0.9300
- Recall: 0.9415

## 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: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.3523        | 1.0   | 782  | 0.1934          | 0.927    | 0.9261 | 0.9402    | 0.9124 |
| 0.1479        | 2.0   | 1564 | 0.2261          | 0.9291   | 0.9277 | 0.9489    | 0.9074 |
| 0.0702        | 3.0   | 2346 | 0.2594          | 0.9352   | 0.9358 | 0.9300    | 0.9415 |


### Framework versions

- Transformers 4.26.0
- Pytorch 1.13.1+cu117
- Datasets 2.9.0
- Tokenizers 0.13.2
