Text Classification
sentence-transformers
Joblib
Scikit-learn
French
cyberbullying
harassment
social-media
french
Eval Results (legacy)
Instructions to use DataForGood/balance-tes-haters-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use DataForGood/balance-tes-haters-classifier with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("DataForGood/balance-tes-haters-classifier") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Scikit-learn
How to use DataForGood/balance-tes-haters-classifier with Scikit-learn:
from huggingface_hub import hf_hub_download import joblib model = joblib.load( hf_hub_download("DataForGood/balance-tes-haters-classifier", "sklearn_model.joblib") ) # only load pickle files from sources you trust # read more about it here https://skops.readthedocs.io/en/stable/persistence.html - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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# Load components
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clf = joblib.load(hf_hub_download(
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repo_id="
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filename="harassment_arctic_mlp.joblib",
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))
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encoder = SentenceTransformer("Snowflake/snowflake-arctic-embed-l-v2.0")
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# Load components
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clf = joblib.load(hf_hub_download(
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repo_id="DataForGood/balance-tes-haters-classifier",
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filename="harassment_arctic_mlp.joblib",
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))
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encoder = SentenceTransformer("Snowflake/snowflake-arctic-embed-l-v2.0")
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