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---
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
|