Text Classification
Joblib
sentence-transformers
English
scikit-learn
intent-classification
banking77
linear-svc
Instructions to use Amitava25/banking77-intent-classifier-champion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Amitava25/banking77-intent-classifier-champion with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Amitava25/banking77-intent-classifier-champion") 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] - Notebooks
- Google Colab
- Kaggle
Upload champion.json with huggingface_hub
Browse files- champion.json +38 -0
champion.json
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{
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"model_family": "sentence_transformer_linear",
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"dataset_name": "PolyAI/banking77",
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"train_split": "train",
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"test_split": "test",
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"artifacts_dir": "../artifacts/champion",
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"reports_dir": "../reports/champion",
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"text_column": "text",
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"label_column": "label",
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"random_seed": 42,
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"tfidf": {
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"lowercase": true,
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"strip_accents": "unicode",
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"normalization": "none",
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"ngram_range": [1, 2],
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"min_df": 2,
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"max_df": 0.98,
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"sublinear_tf": true,
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"max_features": 50000
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},
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"encoder": {
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"model_name": "BAAI/bge-small-en-v1.5",
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"batch_size": 32,
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"normalize_embeddings": true,
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"device": null
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},
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"classifier": {
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"c": 1.0,
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"class_weight": "balanced",
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"max_iter": 5000,
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"dual": true,
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"random_state": 42
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},
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"analysis": {
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"top_k_confusions": 25,
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"top_k_features_per_class": 20
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}
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}
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