Instructions to use m3hrdadfi/albert-fa-base-v2-sentiment-binary with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use m3hrdadfi/albert-fa-base-v2-sentiment-binary with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="m3hrdadfi/albert-fa-base-v2-sentiment-binary")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("m3hrdadfi/albert-fa-base-v2-sentiment-binary") model = AutoModelForSequenceClassification.from_pretrained("m3hrdadfi/albert-fa-base-v2-sentiment-binary") - Notebooks
- Google Colab
- Kaggle
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## Results
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The model obtained an F1 score of 87.56% for a composition of all three datasets.
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### BibTeX entry and citation info
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## Results
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The model obtained an F1 score of 87.56% for a composition of all three datasets into a binary-labels `Negative` and `Positive`.
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### BibTeX entry and citation info
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