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---
pipeline_tag: text-classification
widget:
- text: "Pani Katarzyno z jakiej racji moja paczka przyszła do sąsiada zamiast do mnie? Nie można poprawnie nadać paczki?"
example_title: "Sentiment"
license: cc-by-4.0
language:
- pl
---
<img src="https://public.3.basecamp.com/p/rs5XqmAuF1iEuW6U7nMHcZeY/upload/download/VL-NLP-short.png" alt="logo voicelab nlp" style="width:300px;"/>
# Sentiment Classification in Polish
```python
import numpy as np
from transformers import AutoTokenizer, AutoModelForSequenceClassification
id2label = {0: "negative", 1: "neutral", 2: "positive"}
tokenizer = AutoTokenizer.from_pretrained("Voicelab/herbert-base-cased-sentiment")
model = AutoModelForSequenceClassification.from_pretrained("Voicelab/herbert-base-cased-sentiment")
input = ["Ale fajnie, spadł dzisiaj śnieg! Ulepimy dziś bałwana?"]
encoding = tokenizer(
input,
add_special_tokens=True,
return_token_type_ids=True,
truncation=True,
padding='max_length',
return_attention_mask=True,
return_tensors='pt',
)
output = model(**encoding).logits.to("cpu").detach().numpy()
prediction = id2label[np.argmax(output)]
print(input, "--->", prediction)
```
Predicted output:
```python
['Ale fajnie, spadł dzisiaj śnieg! Ulepimy dziś bałwana?'] ---> positive
```
### Overview
- **Language model:** [allegro/herbert-base-cased](https://huggingface.co/allegro/herbert-base-cased)
- **Language:** pl
- **Training data:** Reviews + own data
- **Blog post:** [Sentiment analysis - COVID-19 – the source of the heated discussion](https://voicelab.ai/covid-19-the-source-of-the-heated-discussion)
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