Localized Sentiment Models
Collection
A group of sentiment detection models dedicated for specific languages • 2 items • Updated • 1
Twitter Sentiment PL (fast)
Twitter Sentiment PL (fast) is a model based on distiluse 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-fast")
nlp("Szczęście i Opatrzność mają znaczenie Gratuluje @pzpn_pl")
[{'label': 'positive', 'score': 0.9965680837631226}]
Performance
| Metric | Value |
|---|---|
| f1 macro | 0.570 |
| precision macro | 0.570 |
| recall macro | 0.575 |
| accuracy | 0.582 |
| samples per second | 225.9 |
(The performance was evaluated on RTX 3090 gpu)
Changelog
- 2023-07-19: Initial release
License
This model is released under the Apache License 2.0, inherited from the base model sentence-transformers/distiluse-base-multilingual-cased-v1 (Apache 2.0).
Attribution: distiluse-base-multilingual-cased-v1 — Sentence-Transformers (UKP Lab); Twitter Sentiment PL (fast) — bards.ai.
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 info@bards.ai
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Evaluation results
- F1 (macro) on TweetEval (translated to Polish)self-reported0.570
- Precision (macro) on TweetEval (translated to Polish)self-reported0.570
- Recall (macro) on TweetEval (translated to Polish)self-reported0.575
- Accuracy on TweetEval (translated to Polish)self-reported0.582