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