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---
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 |