Text Classification
Transformers
PyTorch
Safetensors
Polish
distilbert
sentiment-analysis
twitter
distiluse
Eval Results (legacy)
text-embeddings-inference
Instructions to use bardsai/twitter-sentiment-pl-fast with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bardsai/twitter-sentiment-pl-fast with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="bardsai/twitter-sentiment-pl-fast")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("bardsai/twitter-sentiment-pl-fast") model = AutoModelForSequenceClassification.from_pretrained("bardsai/twitter-sentiment-pl-fast", device_map="auto") - Notebooks
- Google Colab
- Kaggle
docs: fix model name in description ((base) -> (fast))
Browse files
README.md
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# Twitter Sentiment PL (fast)
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Twitter Sentiment PL (
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The model will give you a three labels: positive, negative and neutral.
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# Twitter Sentiment PL (fast)
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Twitter Sentiment PL (fast) is a model based on [distiluse](https://huggingface.co/sentence-transformers/distiluse-base-multilingual-cased-v1) for analyzing sentiment of Polish twitter posts. It was trained on the translated version of [TweetEval](https://www.researchgate.net/publication/347233661_TweetEval_Unified_Benchmark_and_Comparative_Evaluation_for_Tweet_Classification) by Barbieri et al., 2020 for 10 epochs on single RTX3090 gpu
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The model will give you a three labels: positive, negative and neutral.
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