|
--- |
|
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 |
