readme: ---
  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)
  
  