---
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: sentiment-polish-gpt2-small
  results: []
license: mit
language:
- pl
datasets:
- clarin-pl/polemo2-official
---

<!-- 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-polish-gpt2-small

This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4659
- Accuracy: 0.9627

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data
https://huggingface.co./datasets/clarin-pl/polemo2-official
```bibtex
@inproceedings{kocon-etal-2019-multi,
    title = "Multi-Level Sentiment Analysis of {P}ol{E}mo 2.0: Extended Corpus of Multi-Domain Consumer Reviews",
    author = "Koco{\'n}, Jan  and
      Mi{\l}kowski, Piotr  and
      Za{\'s}ko-Zieli{\'n}ska, Monika",
    booktitle = "Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/K19-1092",
    doi = "10.18653/v1/K19-1092",
    pages = "980--991",
    abstract = "In this article we present an extended version of PolEmo {--} a corpus of consumer reviews from 4 domains: medicine, hotels, products and school. Current version (PolEmo 2.0) contains 8,216 reviews having 57,466 sentences. Each text and sentence was manually annotated with sentiment in 2+1 scheme, which gives a total of 197,046 annotations. We obtained a high value of Positive Specific Agreement, which is 0.91 for texts and 0.88 for sentences. PolEmo 2.0 is publicly available under a Creative Commons copyright license. We explored recent deep learning approaches for the recognition of sentiment, such as Bi-directional Long Short-Term Memory (BiLSTM) and Bidirectional Encoder Representations from Transformers (BERT).",
}
```

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.4049        | 1.0   | 3284  | 0.3351          | 0.8792   |
| 0.1885        | 2.0   | 6568  | 0.2625          | 0.9218   |
| 0.1182        | 3.0   | 9852  | 0.2583          | 0.9419   |
| 0.0825        | 4.0   | 13136 | 0.2886          | 0.9482   |
| 0.0586        | 5.0   | 16420 | 0.3343          | 0.9538   |
| 0.034         | 6.0   | 19704 | 0.3734          | 0.9595   |
| 0.0288        | 7.0   | 22988 | 0.4125          | 0.9599   |
| 0.0185        | 8.0   | 26273 | 0.4262          | 0.9626   |
| 0.0069        | 9.0   | 29557 | 0.4529          | 0.9622   |
| 0.0059        | 10.0  | 32840 | 0.4659          | 0.9627   |


### Framework versions

- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0