---
license: mit
tags:
- generated_from_trainer
datasets:
- sst2
metrics:
- accuracy
model-index:
- name: sentiment-model-saagie
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: sst2
      type: sst2
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.7816666666666666
---

<!-- 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. -->

# sentiment-model-saagie

This model is a fine-tuned version of [prajjwal1/bert-tiny](https://huggingface.co/prajjwal1/bert-tiny) on the sst2 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5403
- Accuracy: 0.7817

## 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: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.523         | 1.0   | 1500 | 0.4783          | 0.7667   |
| 0.3858        | 2.0   | 3000 | 0.5265          | 0.7867   |
| 0.3384        | 3.0   | 4500 | 0.5403          | 0.7817   |


### Framework versions

- Transformers 4.18.0
- Pytorch 1.8.1
- Datasets 2.12.0
- Tokenizers 0.12.1