language: pl
tags:
- text-classification
- sentiment-analysis
- twitter
datasets:
- datasets/tweet_eval
metrics:
- f1
- accuracy
- precision
- recall
widget:
- text: >-
Nigdy przegrana nie sprawiła mi takiej radości. Szczęście i Opatrzność
mają znaczenie Gratuluje @pzpn_pl
example_title: Example 1
- text: >-
Osoby z Ukrainy zapłacą za życie w centrach pomocy? Sprzeczne prawem UE,
niehumanitarne, okrutne.
example_title: Example 2
- text: O której kończycie dzisiaj?
example_title: Example 3
Twitter Sentiment PL (base)
Twitter Sentiment PL (base) is a model based on herbert-base for analyzing sentiment of Polish twitter posts. It was trained on the translated version of TweetEval by Barbieri et al., 2020 for 10 epochs on single RTX3090 gpu
The model will give you a three labels: positive, negative and neutral.
How to use
You can use this model directly with a pipeline for sentiment-analysis:
from transformers import pipeline
nlp = pipeline("sentiment-analysis", model="bardsai/twitter-sentiment-pl-base")
nlp("Nigdy przegrana nie sprawiła mi takiej radości. Szczęście i Opatrzność mają znaczenie Gratuluje @pzpn_pl")
[{'label': 'positive', 'score': 0.9997233748435974}]
Performance
| Metric | Value |
|---|---|
| f1 macro | 0.658 |
| precision macro | 0.655 |
| recall macro | 0.662 |
| accuracy | 0.662 |
| samples per second | 129.9 |
(The performance was evaluated on RTX 3090 gpu)
Changelog
- 2022-12-01: Initial release
- 2023-07-19: Improvement of translation quality
About bards.ai
At bards.ai, we focus on providing machine learning expertise and skills to our partners, particularly in the areas of nlp, machine vision and time series analysis. Our team is located in Wroclaw, Poland. Please visit our website for more information: bards.ai
Let us know if you use our model :). Also, if you need any help, feel free to contact us at [email protected]