language:
- pl
license: apache-2.0
library_name: transformers
pipeline_tag: text-classification
tags:
- text-classification
- sentiment-analysis
- twitter
- distiluse
base_model: sentence-transformers/distiluse-base-multilingual-cased-v1
datasets:
- tweet_eval
metrics:
- f1
- accuracy
- precision
- recall
widget:
- text: 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
model-index:
- name: twitter-sentiment-pl-fast
results:
- task:
type: text-classification
name: Sentiment Analysis
dataset:
name: TweetEval (translated to Polish)
type: tweet_eval
metrics:
- type: f1
value: 0.57
name: F1 (macro)
- type: precision
value: 0.57
name: Precision (macro)
- type: recall
value: 0.575
name: Recall (macro)
- type: accuracy
value: 0.582
name: Accuracy
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