transformers pytorch distilbert text-classification generated_from_trainer dataset:glue license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
---
datasets:
- glue
license: apache-2.0
metrics:
- accuracy
- f1
- precision
- recall
tags:
- generated_from_trainer
model-index:
- name: distilbert_base_uncased_SST2_finetune
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: glue
      type: glue
      config: sst2
      split: validation
      args: sst2
    metrics:
    - type: accuracy
      value: 0.8371559633027523
      name: Accuracy
    - type: f1
      value: 0.8370839311854139
      name: F1
    - type: precision
      value: 0.8373294905842589
      name: Precision
    - type: recall
      value: 0.8371559633027523
      name: Recall
---

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# distilbert_base_uncased_SST2_finetune

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3630
- Accuracy: 0.8372
- F1: 0.8371
- Precision: 0.8373
- Recall: 0.8372
- Learning Rate: 0.0000

## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy | F1     | Precision | Recall | Rate   |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|:------:|
| 0.4616        | 1.0   | 8419  | 0.3845          | 0.8337   | 0.8334 | 0.8350    | 0.8337 | 0.0000 |
| 0.3644        | 2.0   | 16838 | 0.3730          | 0.8291   | 0.8291 | 0.8300    | 0.8291 | 0.0000 |
| 0.3526        | 3.0   | 25257 | 0.3661          | 0.8280   | 0.8277 | 0.8290    | 0.8280 | 0.0000 |
| 0.346         | 4.0   | 33676 | 0.3709          | 0.8349   | 0.8345 | 0.8369    | 0.8349 | 0.0000 |
| 0.3436        | 5.0   | 42095 | 0.3674          | 0.8383   | 0.8383 | 0.8384    | 0.8383 | 0.0000 |
| 0.3412        | 6.0   | 50514 | 0.3630          | 0.8372   | 0.8371 | 0.8373    | 0.8372 | 0.0000 |


### Framework versions

- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
