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my_distilbert_model

Text Classification
Transformers
PyTorch
distilbert
Generated from Trainer
Eval Results
Inference Endpoints
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my_distilbert_model
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---
license: apache-2.0
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: 0.8405253283302064
    - name: F1
      type: f1
      value: 0.8405247669736138
    - name: Precision
      type: precision
      value: 0.8405301230265589
    - name: Recall
      type: recall
      value: 0.8405253283302063
---

<!-- 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: 0.5525
- Accuracy: 0.8405
- F1: 0.8405
- Precision: 0.8405
- Recall: 0.8405

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.4173        | 1.0   | 534  | 0.3898          | 0.8433   | 0.8433 | 0.8433    | 0.8433 |
| 0.2526        | 2.0   | 1068 | 0.4618          | 0.8396   | 0.8395 | 0.8402    | 0.8396 |
| 0.1541        | 3.0   | 1602 | 0.5525          | 0.8405   | 0.8405 | 0.8405    | 0.8405 |


### Framework versions

- Transformers 4.27.2
- Pytorch 2.0.1
- Datasets 2.11.0
- Tokenizers 0.13.2