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@@ -63,7 +63,7 @@ Then you can load this model and run inference.
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  from setfit import SetFitModel
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  # Download from the 🤗 Hub
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- model = SetFitModel.from_pretrained("setfit_model_id")
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  # Run inference
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  preds = model("// quotes are removed | ScannerUtility.java")
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  ```
@@ -92,42 +92,6 @@ preds = model("// quotes are removed | ScannerUtility.java")
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  *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
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  -->
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- ## Training Details
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-
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- ### Training Set Metrics
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- | Training set | Min | Median | Max |
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- |:-------------|:----|:--------|:----|
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- | Word count | 3 | 15.5217 | 299 |
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-
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- ### Training Hyperparameters
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- - batch_size: (32, 32)
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- - num_epochs: (5, 5)
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- - max_steps: -1
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- - sampling_strategy: oversampling
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- - num_iterations: 20
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- - body_learning_rate: (2e-05, 1e-05)
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- - head_learning_rate: 0.01
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- - loss: CosineSimilarityLoss
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- - distance_metric: cosine_distance
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- - margin: 0.25
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- - end_to_end: False
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- - use_amp: False
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- - warmup_proportion: 0.1
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- - l2_weight: 0.01
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- - seed: 42
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- - eval_max_steps: -1
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- - load_best_model_at_end: False
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-
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-
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- ### Framework Versions
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- - Python: 3.10.8
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- - SetFit: 1.1.2
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- - Sentence Transformers: 5.0.0
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- - Transformers: 4.54.1
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- - PyTorch: 2.7.1+cu126
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- - Datasets: 3.6.0
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- - Tokenizers: 0.21.4
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-
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  ## Citation
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  ### BibTeX
 
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  from setfit import SetFitModel
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  # Download from the 🤗 Hub
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+ model = SetFitModel.from_pretrained("NLBSE/nlbse26_java")
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  # Run inference
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  preds = model("// quotes are removed | ScannerUtility.java")
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  ```
 
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  *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
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  -->
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  ## Citation
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  ### BibTeX