---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: finetuning-sentiment-model-3000-samples-6pm
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: imdb
      type: imdb
      args: plain_text
    metrics:
    - name: Precision
      type: precision
      value: 0.875
    - name: Recall
      type: recall
      value: 0.8866666666666667
    - name: F1
      type: f1
      value: 0.880794701986755
    - name: Accuracy
      type: accuracy
      value: 0.88
---

<!-- 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. -->

# finetuning-sentiment-model-3000-samples-6pm

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: 0.2896
- Precision: 0.875
- Recall: 0.8867
- F1: 0.8808
- Accuracy: 0.88

## 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: 1e-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: 11

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 188  | 0.3436          | 0.8633    | 0.8    | 0.8304 | 0.8367   |
| No log        | 2.0   | 376  | 0.2896          | 0.875     | 0.8867 | 0.8808 | 0.88     |
| 0.3           | 3.0   | 564  | 0.3330          | 0.8693    | 0.8867 | 0.8779 | 0.8767   |
| 0.3           | 4.0   | 752  | 0.4378          | 0.8766    | 0.9    | 0.8882 | 0.8867   |
| 0.3           | 5.0   | 940  | 0.5198          | 0.8284    | 0.9333 | 0.8777 | 0.87     |


### Framework versions

- Transformers 4.18.0
- Pytorch 1.10.0+cu111
- Datasets 2.1.0
- Tokenizers 0.12.1