State-of-the-Art Embeddings, Retrieval, and Reranking
This report presents the forensic synthetic code analysis of huggingface/sentence-transformers, a Python project with 19,045 GitHub stars. SynthScan v2.0 examined 137,472 lines of code across 683 source files, recording 3094 pattern matches distributed across 18 syntactic categories. The overall adjusted score of 26.8 places this repository in the Moderate AI signal band.
The scanner applied 160+ deterministic lexical heuristics, multi-line block detectors, abstract syntax tree depth profilers, and a cross-file Jaccard similarity matrix to construct a statistically normalised synthetic code estimate. All matches are individually weighted by severity coefficient and contextual multiplier before summation, and the resulting headline score is temporally discounted to account for the repository's development history relative to the commercial emergence of large language model coding tooling (November 2022 onward).
This chart maps the temporal evolution of the adjusted synthetic code score across successive scan runs. An upward trajectory indicates ongoing incorporation of AI-generated code or expanding LLM-assisted scaffolding; a stable or declining trajectory may reflect active human refactoring, code removal, or the adoption of stricter authorship policies. The dashed secondary line (right axis) independently tracks total raw pattern hit count, which can diverge from the normalised score when codebase size changes significantly between scans.
Classifies detected patterns by their diagnostic confidence and structural impact. CRITICAL patterns (coefficient 10) represent definitive synthetic signatures — hallucinated imports, explicit LLM attribution metadata — virtually never produced by human authors. HIGH (5) indicates strong structural tells such as cross-file repetition or cross-linguistic idioms. MEDIUM (2) covers recognisable conversational padding and AI-specific vocabulary. LOW (1) captures subtle indicators like tautological comments and generic boilerplate that require density to carry independent signal.
This horizontal bar chart decomposes the repository's raw synthetic code score by top-level directory, allowing you to pinpoint precisely which modules or components carry the highest AI authorship density. Directories with disproportionately high scores relative to their size warrant targeted manual review: concentrated AI signatures often trace back to mass-generated configuration layers, auto-ported test suites, LLM-scaffolded boilerplate classes, or entire subsystems authored under heavy copilot assistance. Use this view to prioritise your human code-review effort.
The scanner identified 3094 distinct pattern matches across 18 syntactic categories. Each entry below represents a discrete location in the source code where the engine recorded a statistically significant AI authorship indicator. Expand any category row to inspect the individual file paths, line numbers, code snippets, and the lexical context (CODE, COMMENT, or STRING) in which each match was detected.
Reading the findings table: The Severity column indicates the diagnostic confidence level (CRITICAL / HIGH / MEDIUM / LOW). The Context column identifies whether the match occurred inside executable code, an inline comment, or a string literal — comment-context matches receive a ×1.5 weight because LLMs systematically over-annotate. The ⚡ bolt icon marks clustered matches: three or more patterns within a 10-line window, each receiving an additional ×1.5 density multiplier as dense clusters constitute far stronger evidence of synthetic authorship than isolated hits.
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | …entence_transformer/deprecated_model_card_templates.py | 125 | def model_card_get_pooling_function(pooling_mode): | STRING |
| LOW | sentence_transformers/sentence_transformer/model.py | 1221 | def set_pooling_include_prompt(self, include_prompt: bool) -> None: | CODE |
| LOW | sentence_transformers/sentence_transformer/model.py | 1267 | def get_sentence_embedding_dimension(self) -> int | None: | CODE |
| LOW | sentence_transformers/sentence_transformer/model.py | 1305 | def truncate_sentence_embeddings(self, truncate_dim: int | None) -> Iterator[None]: | CODE |
| LOW | …ntence_transformers/sentence_transformer/model_card.py | 129 | def get_model_specific_metadata(self) -> dict[str, Any]: | CODE |
| LOW | …sentence_transformer/losses/batch_semi_hard_triplet.py | 107 | def compute_loss_from_embeddings(self, embeddings: list[Tensor], labels: Tensor) -> Tensor: | CODE |
| LOW | …sentence_transformer/losses/batch_semi_hard_triplet.py | 113 | def batch_semi_hard_triplet_loss(self, labels: Tensor, embeddings: Tensor) -> Tensor: | CODE |
| LOW | …tence_transformer/losses/multiple_negatives_ranking.py | 237 | def compute_loss_from_embeddings(self, embeddings: list[Tensor], labels: Tensor) -> Tensor: | CODE |
| LOW | …rmers/sentence_transformer/losses/batch_all_triplet.py | 97 | def compute_loss_from_embeddings(self, embeddings: list[Tensor], labels: Tensor) -> Tensor: | CODE |
| LOW | …ransformers/sentence_transformer/losses/contrastive.py | 103 | def compute_loss_from_embeddings(self, embeddings: list[Tensor], labels: Tensor) -> Tensor: | CODE |
| LOW | …ence_transformers/sentence_transformer/losses/angle.py | 83 | def compute_loss_from_embeddings( | CODE |
| LOW | …transformer/losses/global_orthogonal_regularization.py | 140 | def compute_loss_from_embeddings( | CODE |
| LOW | …mers/sentence_transformer/losses/batch_hard_triplet.py | 154 | def compute_loss_from_embeddings(self, embeddings: list[Tensor], labels: Tensor) -> Tensor: | CODE |
| LOW | …mers/sentence_transformer/losses/batch_hard_triplet.py | 231 | def get_anchor_positive_triplet_mask(labels: Tensor) -> Tensor: | CODE |
| LOW⚡ | …mers/sentence_transformer/losses/batch_hard_triplet.py | 250 | def get_anchor_negative_triplet_mask(labels: Tensor) -> Tensor: | CODE |
| LOW | …transformers/sentence_transformer/losses/margin_mse.py | 178 | def compute_loss_from_embeddings(self, embeddings: list[Tensor], labels: Tensor) -> Tensor: | CODE |
| LOW | …mers/sentence_transformer/losses/online_contrastive.py | 82 | def compute_loss_from_embeddings(self, embeddings: list[Tensor], labels: Tensor) -> Tensor: | CODE |
| LOW | …rmers/sentence_transformer/losses/cosine_similarity.py | 84 | def compute_loss_from_embeddings(self, embeddings: list[Tensor], labels: Tensor) -> Tensor: | CODE |
| LOW | …/sentence_transformer/losses/denoising_auto_encoder.py | 16 | def _tie_encoder_decoder_weights(encoder: nn.Module, decoder: nn.Module) -> None: | CODE |
| LOW | …nce_transformers/sentence_transformer/losses/cosent.py | 87 | def compute_loss_from_embeddings(self, embeddings: list[Tensor], labels: Tensor) -> Tensor: | CODE |
| LOW | …ce_transformers/sentence_transformer/losses/triplet.py | 88 | def compute_loss_from_embeddings(self, embeddings: list[Tensor], labels: Tensor) -> Tensor: | CODE |
| LOW | …e_transformer/losses/batch_hard_soft_margin_triplet.py | 97 | def compute_loss_from_embeddings(self, embeddings: list[Tensor], labels: Tensor) -> Tensor: | CODE |
| LOW | …e_transformer/losses/batch_hard_soft_margin_triplet.py | 102 | def batch_hard_triplet_soft_margin_loss(self, labels: Tensor, embeddings: Tensor) -> Tensor: | CODE |
| LOW | …sformers/sentence_transformer/losses/distill_kl_div.py | 174 | def compute_loss_from_embeddings(self, embeddings: list[Tensor], labels: Tensor) -> Tensor: | CODE |
| LOW | …rmers/sentence_transformer/losses/cached_gist_embed.py | 244 | def calculate_loss_and_cache_gradients( | CODE |
| LOW | …nsformers/sentence_transformer/losses/embed_distill.py | 197 | def compute_loss_from_embeddings(self, embeddings: list[Tensor], teacher_embeddings: list[Tensor]) -> Tensor: | CODE |
| LOW | …ntence_transformer/evaluation/binary_classification.py | 317 | def find_best_acc_and_threshold(scores, labels, high_score_more_similar: bool): | CODE |
| LOW | …nsformers/sentence_transformer/evaluation/reranking.py | 292 | def compute_metrics_individual(self, model: SentenceTransformer): | CODE |
| LOW | …ransformers/sentence_transformer/evaluation/triplet.py | 261 | def _get_similarity_functions(self) -> dict: | CODE |
| LOW | …nsformers/sentence_transformer/evaluation/nano_beir.py | 468 | def _load_dataset_subset_split(self, subset: str, split: str, required_columns: list[str]): | CODE |
| LOW | …nsformers/sentence_transformer/evaluation/nano_beir.py | 523 | def store_metrics_in_model_card_data(self, *args, **kwargs): | CODE |
| LOW | …e_transformers/sentence_transformer/modules/pooling.py | 26 | def _convert_legacy_pooling_kwargs(kwargs: dict[str, Any]) -> None: | CODE |
| LOW | …e_transformers/sentence_transformer/modules/pooling.py | 46 | def _deprecated_pooling_mode_kwargs(func): | CODE |
| LOW | …e_transformers/sentence_transformer/modules/pooling.py | 154 | def _exclude_prompt_from_mask(attention_mask: Tensor, prompt_length: int) -> Tensor: | CODE |
| LOW | …ransformers/sentence_transformer/modules/clip_model.py | 36 | def _get_default_modality_config(config: dict[str, Any]) -> tuple[ModalityConfig, str]: | CODE |
| LOW | sentence_transformers/util/deprecated_import.py | 263 | def setup_deprecated_module_imports() -> None: | CODE |
| LOW | sentence_transformers/util/similarity.py | 505 | def _fill_empty_document_scores(scores: Tensor, empty: Tensor) -> Tensor: | CODE |
| LOW | sentence_transformers/util/similarity.py | 879 | def to_similarity_pairwise_fn( | CODE |
| LOW | sentence_transformers/util/tensor.py | 265 | def stack_padded_token_embeddings(embeddings: list[Tensor], masks: list[Tensor]) -> tuple[Tensor, Tensor]: | CODE |
| LOW | sentence_transformers/util/tensor.py | 285 | def cat_padded_token_embeddings(embeddings: list[Tensor], masks: list[Tensor]) -> tuple[Tensor, Tensor]: | CODE |
| LOW | sentence_transformers/util/dataset.py | 56 | def _parse_string_encoded_lists(column: list, col_name: str) -> list: | CODE |
| LOW | sentence_transformers/util/retrieval.py | 89 | def paraphrase_mining_embeddings( | CODE |
| LOW | sentence_transformers/util/environment.py | 33 | def suggest_extra_on_exception() -> Generator[None, None, None]: | CODE |
| LOW | sentence_transformers/util/environment.py | 88 | def check_package_availability(package_name: str, owner: str) -> bool: | CODE |
| LOW | sentence_transformers/util/environment.py | 142 | def check_version_requirements(requirements: dict[str, Any] | None, source: str | None = None) -> None: | CODE |
| LOW | sentence_transformers/util/file_io.py | 34 | def is_sentence_transformer_model( | CODE |
| LOW | sentence_transformers/util/decorators.py | 53 | def transformer_kwargs_decorator(func): | CODE |
| LOW | sentence_transformers/util/decorators.py | 97 | def cross_encoder_init_args_decorator(func): | CODE |
| LOW | sentence_transformers/util/decorators.py | 170 | def cross_encoder_predict_rank_args_decorator(func: F) -> F: | CODE |
| LOW | sentence_transformers/util/decorators.py | 208 | def save_to_hub_args_decorator(func): | CODE |
| LOW | …_transformers/multi_vector_encoder/interpretability.py | 178 | def render_similarity_map_on_image( | CODE |
| LOW | sentence_transformers/multi_vector_encoder/model.py | 1169 | def _load_module_class_from_ref(self, class_ref: str, *args: Any, **kwargs: Any) -> nn.Module: | CODE |
| LOW | sentence_transformers/multi_vector_encoder/model.py | 1175 | def _get_module_init_defaults(self, class_ref: str) -> dict[str, Any]: | CODE |
| LOW | sentence_transformers/multi_vector_encoder/model.py | 1328 | def _build_stanford_projection( | CODE |
| LOW | sentence_transformers/multi_vector_encoder/model.py | 1361 | def _maybe_load_stanford_metadata( | CODE |
| LOW | …ntence_transformers/multi_vector_encoder/model_card.py | 88 | def get_model_specific_metadata(self) -> dict[str, Any]: | CODE |
| LOW | …or_encoder/losses/cached_multiple_negatives_ranking.py | 270 | def calculate_loss_and_cache_gradients( | CODE |
| LOW | …e_transformers/multi_vector_encoder/scoring/colbert.py | 166 | def mean_colbert_scores_pairwise( | CODE |
| LOW | …ransformers/multi_vector_encoder/evaluation/triplet.py | 65 | def _get_similarity_functions(self) -> dict: | CODE |
| LOW | sentence_transformers/backend/optimize.py | 19 | def export_optimized_onnx_model( | CODE |
| 1490 more matches not shown… | ||||
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | sentence_transformers/__init__.py | 1 | CODE | |
| LOW | sentence_transformers/__init__.py | 10 | CODE | |
| LOW | sentence_transformers/__init__.py | 10 | CODE | |
| LOW | sentence_transformers/__init__.py | 10 | CODE | |
| LOW | sentence_transformers/__init__.py | 15 | CODE | |
| LOW | sentence_transformers/__init__.py | 15 | CODE | |
| LOW | sentence_transformers/__init__.py | 16 | CODE | |
| LOW | sentence_transformers/__init__.py | 16 | CODE | |
| LOW | sentence_transformers/__init__.py | 16 | CODE | |
| LOW | sentence_transformers/__init__.py | 16 | CODE | |
| LOW | sentence_transformers/__init__.py | 22 | CODE | |
| LOW | sentence_transformers/__init__.py | 22 | CODE | |
| LOW | sentence_transformers/__init__.py | 22 | CODE | |
| LOW | sentence_transformers/__init__.py | 22 | CODE | |
| LOW | sentence_transformers/__init__.py | 28 | CODE | |
| LOW | sentence_transformers/__init__.py | 28 | CODE | |
| LOW | sentence_transformers/__init__.py | 29 | CODE | |
| LOW | sentence_transformers/__init__.py | 30 | CODE | |
| LOW | sentence_transformers/__init__.py | 31 | CODE | |
| LOW | sentence_transformers/__init__.py | 32 | CODE | |
| LOW | sentence_transformers/__init__.py | 33 | CODE | |
| LOW | sentence_transformers/__init__.py | 34 | CODE | |
| LOW | sentence_transformers/__init__.py | 34 | CODE | |
| LOW | sentence_transformers/__init__.py | 34 | CODE | |
| LOW | sentence_transformers/__init__.py | 34 | CODE | |
| LOW | sentence_transformers/__init__.py | 40 | CODE | |
| LOW | sentence_transformers/__init__.py | 40 | CODE | |
| LOW | sentence_transformers/__init__.py | 42 | CODE | |
| LOW | sentence_transformers/__init__.py | 43 | CODE | |
| LOW | sentence_transformers/__init__.py | 44 | CODE | |
| LOW | …entence_transformer/deprecated_model_card_templates.py | 6 | CODE | |
| LOW | sentence_transformers/sentence_transformer/__init__.py | 1 | CODE | |
| LOW | sentence_transformers/sentence_transformer/__init__.py | 3 | CODE | |
| LOW | sentence_transformers/sentence_transformer/__init__.py | 4 | CODE | |
| LOW | sentence_transformers/sentence_transformer/__init__.py | 5 | CODE | |
| LOW | sentence_transformers/sentence_transformer/__init__.py | 6 | CODE | |
| LOW | sentence_transformers/sentence_transformer/__init__.py | 7 | CODE | |
| LOW | …nce_transformers/sentence_transformer/training_args.py | 1 | CODE | |
| LOW | …nce_transformers/sentence_transformer/training_args.py | 5 | CODE | |
| LOW | …nce_transformers/sentence_transformer/training_args.py | 5 | CODE | |
| LOW | sentence_transformers/sentence_transformer/model.py | 1 | CODE | |
| LOW | …nce_transformers/sentence_transformer/data_collator.py | 1 | CODE | |
| LOW | sentence_transformers/sentence_transformer/trainer.py | 1 | CODE | |
| LOW | sentence_transformers/sentence_transformer/fit_mixin.py | 1 | CODE | |
| LOW | …ntence_transformers/sentence_transformer/model_card.py | 1 | CODE | |
| LOW | …sentence_transformer/losses/batch_semi_hard_triplet.py | 1 | CODE | |
| LOW | …/losses/cached_multiple_negatives_symmetric_ranking.py | 1 | CODE | |
| LOW | …ntence_transformers/sentence_transformer/losses/mse.py | 1 | CODE | |
| LOW | …tence_transformer/losses/multiple_negatives_ranking.py | 1 | CODE | |
| LOW | …transformers/sentence_transformer/losses/gist_embed.py | 1 | CODE | |
| LOW | …rmers/sentence_transformer/losses/batch_all_triplet.py | 1 | CODE | |
| LOW | …ransformers/sentence_transformer/losses/contrastive.py | 1 | CODE | |
| LOW | …ence_transformers/sentence_transformer/losses/angle.py | 1 | CODE | |
| LOW | …ransformer/losses/cached_multiple_negatives_ranking.py | 1 | CODE | |
| LOW | …ransformer/losses/cached_multiple_negatives_ranking.py | 11 | CODE | |
| LOW | …ransformer/losses/cached_multiple_negatives_ranking.py | 11 | CODE | |
| LOW | …ransformer/losses/cached_multiple_negatives_ranking.py | 11 | CODE | |
| LOW | …transformer/losses/global_orthogonal_regularization.py | 1 | CODE | |
| LOW | …mers/sentence_transformer/losses/batch_hard_triplet.py | 1 | CODE | |
| LOW | …transformers/sentence_transformer/losses/margin_mse.py | 1 | CODE | |
| 620 more matches not shown… | ||||
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| HIGH | …nce_transformers/sentence_transformer/training_args.py | 0 | basetrainingarguments extends :class:`~transformers.trainingarguments` with additional arguments specific to sentence tr | STRING |
| HIGH | sentence_transformers/sparse_encoder/training_args.py | 0 | basetrainingarguments extends :class:`~transformers.trainingarguments` with additional arguments specific to sentence tr | STRING |
| HIGH | sentence_transformers/base/training_args.py | 0 | basetrainingarguments extends :class:`~transformers.trainingarguments` with additional arguments specific to sentence tr | STRING |
| HIGH | sentence_transformers/sentence_transformer/trainer.py | 0 | basetrainer is a simple but feature-complete training and eval loop for pytorch based on the 🤗 transformers :class:`~tra | STRING |
| HIGH | sentence_transformers/cross_encoder/trainer.py | 0 | basetrainer is a simple but feature-complete training and eval loop for pytorch based on the 🤗 transformers :class:`~tra | STRING |
| HIGH | sentence_transformers/sparse_encoder/trainer.py | 0 | basetrainer is a simple but feature-complete training and eval loop for pytorch based on the 🤗 transformers :class:`~tra | STRING |
| HIGH | sentence_transformers/base/trainer.py | 0 | basetrainer is a simple but feature-complete training and eval loop for pytorch based on the 🤗 transformers :class:`~tra | STRING |
| HIGH | …sentence_transformer/losses/batch_semi_hard_triplet.py | 0 | batchhardsoftmargintripletloss takes a batch with (input, label) pairs and computes the loss for all possible, valid tri | STRING |
| HIGH | …mers/sentence_transformer/losses/batch_hard_triplet.py | 0 | batchhardsoftmargintripletloss takes a batch with (input, label) pairs and computes the loss for all possible, valid tri | STRING |
| HIGH | …e_transformer/losses/batch_hard_soft_margin_triplet.py | 0 | batchhardsoftmargintripletloss takes a batch with (input, label) pairs and computes the loss for all possible, valid tri | STRING |
| HIGH | …sentence_transformer/losses/batch_semi_hard_triplet.py | 0 | @misc{hermans2017defense, title={in defense of the triplet loss for person re-identification}, author={alexander hermans | STRING |
| HIGH | …rmers/sentence_transformer/losses/batch_all_triplet.py | 0 | @misc{hermans2017defense, title={in defense of the triplet loss for person re-identification}, author={alexander hermans | STRING |
| HIGH | …mers/sentence_transformer/losses/batch_hard_triplet.py | 0 | @misc{hermans2017defense, title={in defense of the triplet loss for person re-identification}, author={alexander hermans | STRING |
| HIGH | …ce_transformers/sentence_transformer/losses/triplet.py | 0 | @misc{hermans2017defense, title={in defense of the triplet loss for person re-identification}, author={alexander hermans | STRING |
| HIGH | …e_transformer/losses/batch_hard_soft_margin_triplet.py | 0 | @misc{hermans2017defense, title={in defense of the triplet loss for person re-identification}, author={alexander hermans | STRING |
| HIGH | …transformers/sentence_transformer/losses/margin_mse.py | 0 | @misc{hofstaetter2020improving, title={improving efficient neural ranking models with cross-architecture knowledge disti | STRING |
| HIGH | …ntence_transformers/cross_encoder/losses/margin_mse.py | 0 | @misc{hofstaetter2020improving, title={improving efficient neural ranking models with cross-architecture knowledge disti | STRING |
| HIGH | …transformers/multi_vector_encoder/losses/margin_mse.py | 0 | @misc{hofstaetter2020improving, title={improving efficient neural ranking models with cross-architecture knowledge disti | STRING |
| HIGH | …ransformers/sentence_transformer/datasets/sentences.py | 0 | this file contains deprecated code that can only be used with the old `model.fit`-style sentence transformers v2.x train | STRING |
| HIGH | …tence_transformer/datasets/no_duplicates_dataloader.py | 0 | this file contains deprecated code that can only be used with the old `model.fit`-style sentence transformers v2.x train | STRING |
| HIGH | …transformers/sentence_transformer/datasets/__init__.py | 0 | this file contains deprecated code that can only be used with the old `model.fit`-style sentence transformers v2.x train | STRING |
| HIGH | …_transformers/sentence_transformer/readers/__init__.py | 0 | this file contains deprecated code that can only be used with the old `model.fit`-style sentence transformers v2.x train | STRING |
| HIGH | …ormers/sentence_transformer/datasets/sentence_label.py | 0 | this file contains deprecated code that can only be used with the old `model.fit`-style sentence transformers v2.x train | STRING |
| HIGH | …rs/sentence_transformer/datasets/parallel_sentences.py | 0 | this file contains deprecated code that can only be used with the old `model.fit`-style sentence transformers v2.x train | STRING |
| HIGH | …_transformers/sentence_transformer/readers/nli_data.py | 0 | this file contains deprecated code that can only be used with the old `model.fit`-style sentence transformers v2.x train | STRING |
| HIGH | …_transformers/sentence_transformer/readers/sts_data.py | 0 | this file contains deprecated code that can only be used with the old `model.fit`-style sentence transformers v2.x train | STRING |
| HIGH | …nsformers/sentence_transformer/readers/paired_files.py | 0 | this file contains deprecated code that can only be used with the old `model.fit`-style sentence transformers v2.x train | STRING |
| HIGH | …sformers/sentence_transformer/readers/input_example.py | 0 | this file contains deprecated code that can only be used with the old `model.fit`-style sentence transformers v2.x train | STRING |
| HIGH | …formers/sentence_transformer/readers/label_sentence.py | 0 | this file contains deprecated code that can only be used with the old `model.fit`-style sentence transformers v2.x train | STRING |
| HIGH | …e_transformers/sentence_transformer/readers/triplet.py | 0 | this file contains deprecated code that can only be used with the old `model.fit`-style sentence transformers v2.x train | STRING |
| HIGH | …entence_transformer/datasets/denoising_auto_encoder.py | 0 | this file contains deprecated code that can only be used with the old `model.fit`-style sentence transformers v2.x train | STRING |
| HIGH | …ransformers/sentence_transformer/modules/clip_model.py | 0 | get the default modality configuration for the current transformer task. returns: tuple[modalityconfig, str]: a tuple of | STRING |
| HIGH | …transformers/sparse_encoder/modules/mlm_transformer.py | 0 | get the default modality configuration for the current transformer task. returns: tuple[modalityconfig, str]: a tuple of | STRING |
| HIGH | sentence_transformers/base/modules/transformer.py | 0 | get the default modality configuration for the current transformer task. returns: tuple[modalityconfig, str]: a tuple of | STRING |
| HIGH | sentence_transformers/cross_encoder/losses/rank_net.py | 0 | get configuration parameters for this loss function. returns: dictionary containing the configuration parameters | STRING |
| HIGH | sentence_transformers/cross_encoder/losses/list_net.py | 0 | get configuration parameters for this loss function. returns: dictionary containing the configuration parameters | STRING |
| HIGH | sentence_transformers/cross_encoder/losses/plist_mle.py | 0 | get configuration parameters for this loss function. returns: dictionary containing the configuration parameters | STRING |
| HIGH | sentence_transformers/cross_encoder/losses/list_mle.py | 0 | get configuration parameters for this loss function. returns: dictionary containing the configuration parameters | STRING |
| HIGH | …tence_transformers/cross_encoder/losses/lambda_loss.py | 0 | get configuration parameters for this loss function. returns: dictionary containing the configuration parameters | STRING |
| HIGH | sentence_transformers/cross_encoder/losses/adr_mse.py | 0 | compute lambdaloss for a batch of queries and their documents. args: inputs: list of (queries, documents_list) labels: g | STRING |
| HIGH | sentence_transformers/cross_encoder/losses/list_net.py | 0 | compute lambdaloss for a batch of queries and their documents. args: inputs: list of (queries, documents_list) labels: g | STRING |
| HIGH | sentence_transformers/cross_encoder/losses/plist_mle.py | 0 | compute lambdaloss for a batch of queries and their documents. args: inputs: list of (queries, documents_list) labels: g | STRING |
| HIGH | …tence_transformers/cross_encoder/losses/lambda_loss.py | 0 | compute lambdaloss for a batch of queries and their documents. args: inputs: list of (queries, documents_list) labels: g | STRING |
| HIGH | tests/sentence_transformer/test_model_card.py | 0 | dummy dataset for testing purposes. the dataset looks as follows: { "anchor": ["anchor 1", "anchor 2", ..., "anchor 10"] | STRING |
| HIGH | tests/cross_encoder/test_model_card.py | 0 | dummy dataset for testing purposes. the dataset looks as follows: { "anchor": ["anchor 1", "anchor 2", ..., "anchor 10"] | STRING |
| HIGH | tests/sparse_encoder/test_model_card.py | 0 | dummy dataset for testing purposes. the dataset looks as follows: { "anchor": ["anchor 1", "anchor 2", ..., "anchor 10"] | STRING |
| HIGH | tests/sentence_transformer/test_compute_embeddings.py | 0 | predict() must run the forward pass via __call__ so that model.compile() applies to inference. | STRING |
| HIGH | tests/multi_vector_encoder/test_model.py | 0 | predict() must run the forward pass via __call__ so that model.compile() applies to inference. | STRING |
| HIGH | tests/sparse_encoder/test_model.py | 0 | predict() must run the forward pass via __call__ so that model.compile() applies to inference. | STRING |
| HIGH | tests/cross_encoder/test_model.py | 0 | predict() must run the forward pass via __call__ so that model.compile() applies to inference. | STRING |
| HIGH | …ransformer/evaluation/test_label_accuracy_evaluator.py | 0 | tests the correct computation of evaluation scores from binaryclassificationevaluator | STRING |
| HIGH | …mer/evaluation/test_binary_classification_evaluator.py | 0 | tests the correct computation of evaluation scores from binaryclassificationevaluator | STRING |
| HIGH | …sformer/evaluation/test_paraphrase_mining_evaluator.py | 0 | tests the correct computation of evaluation scores from binaryclassificationevaluator | STRING |
| HIGH | tests/base/modules/transformer/test_text_generation.py | 0 | create a transformer instance and return it with its supported modalities. | STRING |
| HIGH | tests/base/modules/transformer/test_fill_mask.py | 0 | create a transformer instance and return it with its supported modalities. | STRING |
| HIGH | tests/base/modules/transformer/test_any_to_any.py | 0 | create a transformer instance and return it with its supported modalities. | STRING |
| HIGH | …se/modules/transformer/test_sequence_classification.py | 0 | create a transformer instance and return it with its supported modalities. | STRING |
| HIGH | tests/base/modules/transformer/test_text_generation.py | 0 | test inference with each supported modality (single and multi-modal). | STRING |
| HIGH | …ts/base/modules/transformer/test_feature_extraction.py | 0 | test inference with each supported modality (single and multi-modal). | STRING |
| HIGH | tests/base/modules/transformer/test_any_to_any.py | 0 | test inference with each supported modality (single and multi-modal). | STRING |
| 55 more matches not shown… | ||||
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| MEDIUM | sentence_transformers/sentence_transformer/model.py | 1151 | # Create a pool if not provided, but a list of devices is | COMMENT |
| MEDIUM | …transformers/sentence_transformer/losses/gist_embed.py | 195 | # Define the anchor threshold | COMMENT |
| MEDIUM | …rmers/sentence_transformer/losses/cached_gist_embed.py | 298 | # Define the anchor threshold | COMMENT |
| MEDIUM | …s/sentence_transformer/evaluation/paraphrase_mining.py | 66 | # Create a mapping from qid to question & a list of duplicates (qid1, qid2) | STRING |
| MEDIUM | sentence_transformers/util/retrieval.py | 163 | """This function is deprecated. Use semantic_search instead""" | STRING |
| MEDIUM | sentence_transformers/cross_encoder/model.py | 301 | # Create a pool if is not provided, but a list of devices is | COMMENT |
| MEDIUM | …ers/cross_encoder/losses/multiple_negatives_ranking.py | 137 | # Create a mask for each anchor to each candidate index, where the matching positive | COMMENT |
| MEDIUM | sentence_transformers/cross_encoder/losses/plist_mle.py | 224 | # Create a mask for valid entries | COMMENT |
| MEDIUM | …ransformers/cross_encoder/evaluation/classification.py | 51 | # Create a list of pairs, and map the labels to the labels that the model knows | STRING |
| MEDIUM | sentence_transformers/sparse_encoder/model.py | 957 | # Create a pool if not provided, but a list of devices is | COMMENT |
| MEDIUM | sentence_transformers/base/sampler.py | 567 | # Create a random numpy permutation using int32 (or int64 if necessary) | COMMENT |
| MEDIUM | sentence_transformers/base/modules/transformer.py | 2778 | # This method is only called if this model has a modules.json, i.e. it's already been saved | COMMENT |
| MEDIUM | sentence_transformers/base/modules/router.py | 86 | # Create an asymmetric model with different encoders for queries and documents | COMMENT |
| MEDIUM⚡ | tests/sentence_transformer/test_model.py | 1217 | # Create a simple dataset with a text column | COMMENT |
| MEDIUM⚡ | tests/sentence_transformer/test_model.py | 1338 | # Create a Router with mixed modules | COMMENT |
| MEDIUM | tests/sentence_transformer/test_model.py | 1002 | # Create a mock model with required prompts | COMMENT |
| MEDIUM | tests/sentence_transformer/test_model.py | 1059 | # Create a mock model with required prompts | COMMENT |
| MEDIUM | tests/sentence_transformer/test_multi_process.py | 89 | # Create a pool | COMMENT |
| MEDIUM | tests/sentence_transformer/test_multi_process.py | 186 | # Create a pool | COMMENT |
| MEDIUM | tests/sentence_transformer/test_trainer.py | 200 | # Create a new model card if a Trainer was initialized | COMMENT |
| MEDIUM | tests/sentence_transformer/test_trainer.py | 833 | # Define a custom batch sampler function | COMMENT |
| MEDIUM | tests/sentence_transformer/test_trainer.py | 963 | # Define a custom multi-dataset batch sampler function | COMMENT |
| MEDIUM | tests/util/test_hard_negatives.py | 1308 | # Create a larger dataset with 32 entries | COMMENT |
| MEDIUM | tests/util/test_hard_negatives.py | 1423 | # Create a dataset with just 2 pairs | COMMENT |
| MEDIUM⚡ | tests/cross_encoder/test_model.py | 802 | # Create a simple dataset with a text column | COMMENT |
| MEDIUM | tests/cross_encoder/test_trainer.py | 101 | # Create a new model card if a Trainer was initialized | COMMENT |
| MEDIUM⚡ | tests/sparse_encoder/test_model.py | 426 | # Create a simple dataset with a text column | COMMENT |
| MEDIUM | tests/sparse_encoder/test_model.py | 136 | # Create an empty sparse tensor | COMMENT |
| MEDIUM | tests/sparse_encoder/test_model.py | 171 | # Create a batch where the first sample has values but the second is all zeros | COMMENT |
| MEDIUM | tests/sparse_encoder/test_multi_process.py | 62 | # Create a pool | COMMENT |
| MEDIUM | tests/sparse_encoder/modules/test_csr.py | 11 | # Create a wrapper to measure outputs of the forward method | COMMENT |
| MEDIUM | tests/sparse_encoder/modules/test_csr.py | 61 | # Create the wrapper and replace the forward method | COMMENT |
| MEDIUM | tests/base/samplers/test_no_duplicates_batch_sampler.py | 52 | # Create a list of two 0's, two 1's, two 2's, ... two 49's. Then shuffle. | COMMENT |
| MEDIUM⚡ | tests/base/modules/test_router.py | 733 | # Create a Router with different module configurations for each route | COMMENT |
| MEDIUM⚡ | tests/base/modules/test_router.py | 743 | # Create a SentenceTransformer with static_embedding followed by router | COMMENT |
| MEDIUM | tests/base/modules/test_router.py | 95 | # Create a custom ModuleDict subclass to track access | COMMENT |
| MEDIUM | tests/base/modules/test_router.py | 237 | # Create a Router with StaticEmbedding modules | COMMENT |
| MEDIUM | tests/base/modules/test_router.py | 363 | # Create a Router with StaticEmbedding modules | COMMENT |
| MEDIUM | tests/base/modules/test_router.py | 489 | # Create a Router with StaticEmbedding modules | COMMENT |
| MEDIUM | tests/base/modules/test_router.py | 507 | # Create a loss function that works with router | COMMENT |
| MEDIUM | tests/base/modules/test_router.py | 531 | # Create a Router with StaticEmbedding modules | COMMENT |
| MEDIUM | tests/base/modules/test_router.py | 543 | # Create a loss function that works with router | COMMENT |
| MEDIUM | tests/base/modules/test_router.py | 1407 | # Create an object that will fail modality inference | COMMENT |
| MEDIUM | tests/base/modules/test_dense.py | 92 | # Create a Dense layer with custom keys | COMMENT |
| MEDIUM | …raining/data_augmentation/train_sts_qqp_crossdomain.py | 182 | # Define the training arguments | COMMENT |
| MEDIUM | …/training/data_augmentation/train_sts_indomain_bm25.py | 216 | # Define the training arguments | COMMENT |
| MEDIUM | …/training/data_augmentation/train_sts_indomain_bm25.py | 237 | # Create the trainer & start training | COMMENT |
| MEDIUM | …raining/data_augmentation/train_sts_indomain_nlpaug.py | 137 | # Define the training arguments | COMMENT |
| MEDIUM | …raining/data_augmentation/train_sts_indomain_nlpaug.py | 158 | # Create the trainer & start training | COMMENT |
| MEDIUM | …ining/data_augmentation/train_sts_indomain_semantic.py | 226 | # Define the training arguments | COMMENT |
| MEDIUM | …transformer/training/other/training_batch_hard_trec.py | 45 | # Create a dev set from train set | COMMENT |
| MEDIUM | …ansformer/training/other/training_gooaq_infonce_gor.py | 42 | # Define a custom loss that combines InfoNCE and Global Orthogonal Regularization | COMMENT |
| MEDIUM | …ransformer/training/distillation/model_distillation.py | 111 | # Create a relatively small dataset for evaluation | COMMENT |
| MEDIUM | …ransformer/training/distillation/model_distillation.py | 116 | # Create an STSB evaluator | COMMENT |
| MEDIUM | …ransformer/training/distillation/model_distillation.py | 165 | # Define the training arguments | COMMENT |
| MEDIUM | …ransformer/training/distillation/model_distillation.py | 189 | # Create the trainer & start training | COMMENT |
| MEDIUM | …ing/distillation/model_distillation_layer_reduction.py | 58 | # Create a smaller student model by using only some of the teacher layers | COMMENT |
| MEDIUM | …ing/distillation/model_distillation_layer_reduction.py | 134 | # Create a relatively small dataset for evaluation | COMMENT |
| MEDIUM | …ing/distillation/model_distillation_layer_reduction.py | 169 | # Create an STSB evaluator | COMMENT |
| MEDIUM | …ing/distillation/model_distillation_layer_reduction.py | 188 | # Define the training arguments | COMMENT |
| 34 more matches not shown… | ||||
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | …entence_transformer/deprecated_model_card_templates.py | 189 | except Exception as e: | CODE |
| MEDIUM | …entence_transformer/deprecated_model_card_templates.py | 160 | def get_train_objective_info(dataloader, loss): | CODE |
| LOW | …nsformers/sentence_transformer/evaluation/nano_beir.py | 477 | except Exception as e: | CODE |
| LOW | sentence_transformers/util/hard_negatives.py | 515 | except Exception: | CODE |
| LOW | sentence_transformers/util/logging.py | 19 | except Exception: | CODE |
| MEDIUM | sentence_transformers/util/logging.py | 12 | def emit(self, record) -> None: | CODE |
| LOW | sentence_transformers/util/misc.py | 148 | except Exception: | CODE |
| LOW | sentence_transformers/util/environment.py | 192 | except Exception as e: | CODE |
| LOW | sentence_transformers/util/file_io.py | 192 | except Exception as first_error: | CODE |
| LOW | sentence_transformers/util/file_io.py | 249 | except Exception: | CODE |
| LOW | sentence_transformers/multi_vector_encoder/model.py | 1648 | except Exception as e: | CODE |
| LOW | sentence_transformers/multi_vector_encoder/model.py | 1652 | except Exception: | CODE |
| LOW | sentence_transformers/cross_encoder/model.py | 387 | except Exception as e: | CODE |
| LOW | sentence_transformers/cross_encoder/model.py | 391 | except Exception: | CODE |
| LOW | …nce_transformers/cross_encoder/evaluation/nano_beir.py | 398 | except Exception as e: | CODE |
| LOW | sentence_transformers/sparse_encoder/model.py | 1022 | except Exception as e: | CODE |
| LOW | sentence_transformers/sparse_encoder/model.py | 1026 | except Exception: | CODE |
| LOW | sentence_transformers/base/model.py | 842 | except Exception: | CODE |
| LOW | sentence_transformers/base/model.py | 1008 | except Exception as exc: | CODE |
| LOW | sentence_transformers/base/model.py | 1184 | except Exception as exc: | CODE |
| LOW | sentence_transformers/base/model.py | 1194 | except Exception: | CODE |
| LOW | sentence_transformers/base/modality.py | 457 | except Exception as exc: | CODE |
| LOW | sentence_transformers/base/trainer.py | 695 | except Exception: | CODE |
| LOW | sentence_transformers/base/trainer.py | 700 | except Exception as exc: | CODE |
| LOW | sentence_transformers/base/sampler.py | 373 | except Exception: | CODE |
| MEDIUM | sentence_transformers/base/model_card.py | 1445 | def try_to_float(metric_value): | CODE |
| LOW⚡ | sentence_transformers/base/model_card.py | 1339 | except Exception: | CODE |
| LOW⚡ | sentence_transformers/base/model_card.py | 1963 | except Exception as exc: | CODE |
| LOW⚡ | sentence_transformers/base/model_card.py | 1971 | except Exception as exc: | CODE |
| LOW⚡ | sentence_transformers/base/model_card.py | 1979 | except Exception as exc: | CODE |
| LOW⚡ | sentence_transformers/base/model_card.py | 1986 | except Exception as exc: | CODE |
| LOW | sentence_transformers/base/model_card.py | 456 | except Exception: | CODE |
| LOW | sentence_transformers/base/model_card.py | 869 | except Exception: | CODE |
| LOW | sentence_transformers/base/model_card.py | 912 | except Exception: | CODE |
| LOW | sentence_transformers/base/model_card.py | 1408 | except Exception: | CODE |
| LOW | sentence_transformers/base/model_card.py | 1448 | except Exception: | CODE |
| LOW | sentence_transformers/base/model_card.py | 1578 | except Exception: | CODE |
| LOW | sentence_transformers/base/model_card.py | 1586 | except Exception: | CODE |
| LOW | sentence_transformers/base/model_card.py | 1608 | except Exception: | CODE |
| LOW | sentence_transformers/base/model_card.py | 1863 | except Exception: | CODE |
| LOW | sentence_transformers/base/model_card.py | 1920 | except Exception: | CODE |
| LOW | sentence_transformers/base/model_card.py | 1931 | except Exception as exc: | CODE |
| LOW | sentence_transformers/base/model_card.py | 1937 | except Exception as exc: | CODE |
| LOW | sentence_transformers/base/model_card.py | 1948 | except Exception as exc: | CODE |
| LOW | sentence_transformers/base/model_card.py | 495 | except Exception: | STRING |
| LOW | sentence_transformers/base/model_card.py | 545 | except Exception: | STRING |
| LOW | sentence_transformers/base/modules/transformer.py | 2102 | except Exception as exc: | CODE |
| LOW | sentence_transformers/base/modules/transformer.py | 2140 | except Exception as exc: # never let suffix derivation break tokenization | CODE |
| LOW | sentence_transformers/base/modules/transformer.py | 2569 | except Exception: | CODE |
| LOW | tests/cross_encoder/test_model_card.py | 290 | except Exception: | CODE |
| LOW | tests/sparse_encoder/test_pretrained.py | 152 | except Exception as e: | CODE |
| LOW | tests/sparse_encoder/test_pretrained.py | 163 | except Exception as e: | CODE |
| LOW | tests/sparse_encoder/test_pretrained.py | 193 | except Exception as e: | CODE |
| LOW | tests/sparse_encoder/test_trainer.py | 127 | except Exception as e: | CODE |
| LOW | tests/base/test_model_card.py | 356 | except Exception: | CODE |
| LOW⚡ | tests/base/test_modality.py | 821 | except Exception as exc: | CODE |
| LOW | tests/base/modules/transformer/test_text_generation.py | 87 | except Exception as e: | CODE |
| LOW | tests/base/modules/transformer/conftest.py | 197 | except Exception as e: | CODE |
| LOW | tests/base/modules/transformer/conftest.py | 722 | except Exception: | CODE |
| LOW | tests/base/modules/transformer/test_fill_mask.py | 78 | except Exception as e: | CODE |
| 134 more matches not shown… | ||||
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | sentence_transformers/__init__.py | 57 | __all__ = [ | CODE |
| LOW | sentence_transformers/sentence_transformer/__init__.py | 9 | __all__ = [ | CODE |
| LOW | …nce_transformers/sentence_transformer/training_args.py | 53 | __all__ = ["SentenceTransformerTrainingArguments", "BatchSamplers", "MultiDatasetBatchSamplers"] | STRING |
| LOW | sentence_transformers/sentence_transformer/model.py | 1221 | def set_pooling_include_prompt(self, include_prompt: bool) -> None: | CODE |
| LOW | sentence_transformers/sentence_transformer/trainer.py | 33 | logger = logging.getLogger(__name__) | CODE |
| LOW | sentence_transformers/sentence_transformer/fit_mixin.py | 32 | logger = logging.getLogger(__name__) | CODE |
| LOW | …tence_transformer/losses/multiple_negatives_ranking.py | 14 | logger = logging.getLogger(__name__) | CODE |
| LOW | …e_transformers/sentence_transformer/losses/__init__.py | 38 | __all__ = [ | CODE |
| LOW | …transformers/sentence_transformer/losses/matryoshka.py | 15 | logger = logging.getLogger(__name__) | CODE |
| LOW | …transformers/sentence_transformer/losses/matryoshka.py | 46 | def set_dim(self, dim) -> None: | CODE |
| LOW | …/sentence_transformer/losses/denoising_auto_encoder.py | 13 | logger = logging.getLogger(__name__) | CODE |
| LOW | …sformers/sentence_transformer/losses/adaptive_layer.py | 33 | def set_layer_idx(self, layer_idx) -> None: | CODE |
| LOW | …ce_transformers/sentence_transformer/losses/softmax.py | 16 | logger = logging.getLogger(__name__) | CODE |
| LOW | …transformers/sentence_transformer/datasets/__init__.py | 17 | __all__ = [ | CODE |
| LOW | …ormers/sentence_transformer/datasets/sentence_label.py | 20 | logger = logging.getLogger(__name__) | CODE |
| LOW | …rs/sentence_transformer/datasets/parallel_sentences.py | 22 | logger = logging.getLogger(__name__) | CODE |
| LOW | …ce_transformers/sentence_transformer/evaluation/mse.py | 15 | logger = logging.getLogger(__name__) | CODE |
| LOW | …ntence_transformer/evaluation/information_retrieval.py | 21 | logger = logging.getLogger(__name__) | CODE |
| LOW | …/sentence_transformer/evaluation/mse_from_dataframe.py | 17 | logger = logging.getLogger(__name__) | CODE |
| LOW | …ansformers/sentence_transformer/evaluation/__init__.py | 20 | __all__ = [ | CODE |
| LOW | …ntence_transformer/evaluation/binary_classification.py | 24 | logger = logging.getLogger(__name__) | CODE |
| LOW | …nsformers/sentence_transformer/evaluation/reranking.py | 24 | logger = logging.getLogger(__name__) | CODE |
| LOW | …formers/sentence_transformer/evaluation/translation.py | 19 | logger = logging.getLogger(__name__) | CODE |
| LOW | …ransformers/sentence_transformer/evaluation/triplet.py | 25 | logger = logging.getLogger(__name__) | CODE |
| LOW | …s/sentence_transformer/evaluation/paraphrase_mining.py | 15 | logger = logging.getLogger(__name__) | CODE |
| LOW | …mers/sentence_transformer/evaluation/label_accuracy.py | 17 | logger = logging.getLogger(__name__) | CODE |
| LOW | …nsformers/sentence_transformer/evaluation/nano_beir.py | 22 | logger = logging.getLogger(__name__) | CODE |
| LOW | …entence_transformer/evaluation/embedding_similarity.py | 24 | logger = logging.getLogger(__name__) | CODE |
| LOW | …_transformers/sentence_transformer/readers/__init__.py | 18 | __all__ = [ | CODE |
| LOW | …_transformers/sentence_transformer/modules/__init__.py | 23 | __all__ = [ | CODE |
| LOW | …ransformers/sentence_transformer/modules/clip_model.py | 8 | logger = logging.getLogger(__name__) | CODE |
| LOW | …tence_transformers/sentence_transformer/modules/bow.py | 13 | logger = logging.getLogger(__name__) | CODE |
| LOW | …ormers/sentence_transformer/modules/word_embeddings.py | 24 | logger = logging.getLogger(__name__) | CODE |
| LOW | …rmers/sentence_transformer/modules/static_embedding.py | 25 | logger = logging.getLogger(__name__) | CODE |
| LOW | …nsformers/sentence_transformer/modules/word_weights.py | 10 | logger = logging.getLogger(__name__) | CODE |
| LOW | …ers/sentence_transformer/modules/tokenizer/__init__.py | 7 | __all__ = [ | CODE |
| LOW | …rmers/sentence_transformer/modules/tokenizer/phrase.py | 14 | logger = logging.getLogger(__name__) | CODE |
| LOW | sentence_transformers/util/hard_negatives.py | 16 | logger = logging.getLogger(__name__) | CODE |
| LOW | sentence_transformers/util/misc.py | 17 | logger = logging.getLogger(__name__) | CODE |
| LOW | sentence_transformers/util/quantization.py | 10 | logger = logging.getLogger(__name__) | CODE |
| LOW | sentence_transformers/util/__init__.py | 69 | __all__ = [ | CODE |
| LOW | sentence_transformers/util/retrieval.py | 17 | logger = logging.getLogger(__name__) | CODE |
| LOW | sentence_transformers/util/environment.py | 18 | logger = logging.getLogger(__name__) | CODE |
| LOW | sentence_transformers/util/file_io.py | 11 | logger = logging.getLogger(__name__) | CODE |
| LOW | …_transformers/multi_vector_encoder/interpretability.py | 275 | __all__ = [ | CODE |
| LOW | sentence_transformers/multi_vector_encoder/__init__.py | 11 | __all__ = [ | CODE |
| LOW | sentence_transformers/multi_vector_encoder/trainer.py | 33 | logger = logging.getLogger(__name__) | CODE |
| LOW | …ntence_transformers/multi_vector_encoder/model_card.py | 16 | logger = logging.getLogger(__name__) | CODE |
| LOW | …e_transformers/multi_vector_encoder/losses/__init__.py | 8 | __all__ = [ | CODE |
| LOW | …_transformers/multi_vector_encoder/scoring/__init__.py | 13 | __all__ = [ | CODE |
| LOW | …ansformers/multi_vector_encoder/evaluation/__init__.py | 9 | __all__ = [ | CODE |
| LOW | …ormers/multi_vector_encoder/evaluation/distillation.py | 21 | logger = logging.getLogger(__name__) | CODE |
| LOW | …_transformers/multi_vector_encoder/modules/__init__.py | 6 | __all__ = [ | CODE |
| LOW | sentence_transformers/backend/optimize.py | 8 | logger = logging.getLogger(__name__) | CODE |
| LOW | sentence_transformers/backend/quantize.py | 9 | logger = logging.getLogger(__name__) | CODE |
| LOW | sentence_transformers/backend/__init__.py | 8 | __all__ = [ | CODE |
| LOW | sentence_transformers/backend/utils.py | 18 | logger = logging.getLogger(__name__) | CODE |
| LOW | sentence_transformers/backend/load.py | 11 | logger = logging.getLogger(__name__) | CODE |
| LOW | sentence_transformers/cross_encoder/__init__.py | 9 | __all__ = [ | CODE |
| LOW | sentence_transformers/cross_encoder/trainer.py | 31 | logger = logging.getLogger(__name__) | CODE |
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| LOW | sentence_transformers/sentence_transformer/model.py | 155 | CODE | |
| LOW | sentence_transformers/sentence_transformer/model.py | 228 | CODE | |
| LOW | sentence_transformers/sentence_transformer/model.py | 248 | CODE | |
| LOW | sentence_transformers/sentence_transformer/model.py | 269 | CODE | |
| LOW | sentence_transformers/sentence_transformer/model.py | 290 | CODE | |
| LOW | sentence_transformers/sentence_transformer/model.py | 312 | CODE | |
| LOW | sentence_transformers/sentence_transformer/model.py | 332 | CODE | |
| LOW | sentence_transformers/sentence_transformer/model.py | 391 | CODE | |
| LOW | sentence_transformers/sentence_transformer/model.py | 411 | CODE | |
| LOW | sentence_transformers/sentence_transformer/model.py | 432 | CODE | |
| LOW | sentence_transformers/sentence_transformer/model.py | 453 | CODE | |
| LOW | sentence_transformers/sentence_transformer/model.py | 475 | CODE | |
| LOW | sentence_transformers/sentence_transformer/model.py | 495 | CODE | |
| LOW | sentence_transformers/sentence_transformer/model.py | 565 | CODE | |
| LOW | sentence_transformers/sentence_transformer/model.py | 585 | CODE | |
| LOW | sentence_transformers/sentence_transformer/model.py | 606 | CODE | |
| LOW | sentence_transformers/sentence_transformer/model.py | 627 | CODE | |
| LOW | sentence_transformers/sentence_transformer/model.py | 648 | CODE | |
| LOW | sentence_transformers/sentence_transformer/model.py | 669 | CODE | |
| LOW | sentence_transformers/sentence_transformer/model.py | 690 | CODE | |
| LOW | sentence_transformers/sentence_transformer/model.py | 711 | CODE | |
| LOW | sentence_transformers/sentence_transformer/model.py | 733 | CODE | |
| LOW | sentence_transformers/sentence_transformer/model.py | 754 | CODE | |
| LOW | sentence_transformers/sentence_transformer/model.py | 1099 | CODE | |
| LOW | sentence_transformers/sentence_transformer/trainer.py | 118 | CODE | |
| LOW | sentence_transformers/sentence_transformer/fit_mixin.py | 160 | CODE | |
| LOW | sentence_transformers/sentence_transformer/fit_mixin.py | 456 | CODE | |
| LOW | …ransformer/losses/cached_multiple_negatives_ranking.py | 27 | CODE | |
| LOW | …nsformers/sentence_transformer/losses/matryoshka_2d.py | 15 | CODE | |
| LOW | …rmers/sentence_transformer/losses/cached_gist_embed.py | 32 | CODE | |
| LOW | …ntence_transformer/evaluation/information_retrieval.py | 131 | CODE | |
| LOW | …nsformers/sentence_transformer/evaluation/reranking.py | 91 | CODE | |
| LOW | …ransformers/sentence_transformer/evaluation/triplet.py | 93 | CODE | |
| LOW | …s/sentence_transformer/evaluation/paraphrase_mining.py | 92 | CODE | |
| LOW | …nsformers/sentence_transformer/evaluation/nano_beir.py | 214 | CODE | |
| LOW | …entence_transformer/evaluation/embedding_similarity.py | 82 | CODE | |
| LOW | …_transformers/sentence_transformer/readers/sts_data.py | 26 | CODE | |
| LOW | …_transformers/sentence_transformer/readers/sts_data.py | 78 | CODE | |
| LOW | …ormers/sentence_transformer/modules/word_embeddings.py | 91 | CODE | |
| LOW | …rmers/sentence_transformer/modules/static_embedding.py | 169 | CODE | |
| LOW | sentence_transformers/util/hard_negatives.py | 25 | CODE | |
| LOW | sentence_transformers/util/quantization.py | 18 | CODE | |
| LOW | sentence_transformers/util/quantization.py | 202 | CODE | |
| LOW | sentence_transformers/util/retrieval.py | 23 | CODE | |
| LOW | sentence_transformers/multi_vector_encoder/model.py | 157 | CODE | |
| LOW | sentence_transformers/multi_vector_encoder/model.py | 225 | CODE | |
| LOW | sentence_transformers/multi_vector_encoder/model.py | 243 | CODE | |
| LOW | sentence_transformers/multi_vector_encoder/model.py | 262 | CODE | |
| LOW | sentence_transformers/multi_vector_encoder/model.py | 281 | CODE | |
| LOW | sentence_transformers/multi_vector_encoder/model.py | 299 | CODE | |
| LOW | sentence_transformers/multi_vector_encoder/model.py | 318 | CODE | |
| LOW | sentence_transformers/multi_vector_encoder/model.py | 338 | CODE | |
| LOW | sentence_transformers/multi_vector_encoder/model.py | 355 | CODE | |
| LOW | sentence_transformers/multi_vector_encoder/model.py | 401 | CODE | |
| LOW | sentence_transformers/multi_vector_encoder/model.py | 419 | CODE | |
| LOW | sentence_transformers/multi_vector_encoder/model.py | 438 | CODE | |
| LOW | sentence_transformers/multi_vector_encoder/model.py | 457 | CODE | |
| LOW | sentence_transformers/multi_vector_encoder/model.py | 475 | CODE | |
| LOW | sentence_transformers/multi_vector_encoder/model.py | 494 | CODE | |
| LOW | sentence_transformers/multi_vector_encoder/model.py | 514 | CODE | |
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| HIGH | sentence_transformers/sentence_transformer/trainer.py | 37 | SentenceTransformerTrainer is a simple but feature-complete training and eval loop for PyTorch based on the 🤗 T | STRING |
| HIGH | …rmers/sentence_transformer/modules/static_embedding.py | 182 | Creates a StaticEmbedding instance from a distillation process using the `model2vec` package. Args: | STRING |
| HIGH | …rmers/sentence_transformer/modules/static_embedding.py | 245 | Create a StaticEmbedding instance from a model2vec model. This method loads a pre-trained model2vec model | STRING |
| HIGH | sentence_transformers/util/misc.py | 120 | Import a dotted module path and return the attribute/class designated by the last name in the path. Raise Impor | STRING |
| HIGH | sentence_transformers/util/misc.py | 169 | Resolve a module class reference to a class object. For class refs in the ``sentence_transformers.*`` namespac | STRING |
| HIGH | sentence_transformers/util/similarity.py | 840 | Converts a similarity function name or enum value to the corresponding similarity function. Args: | STRING |
| HIGH | sentence_transformers/util/similarity.py | 882 | Converts a similarity function into a pairwise similarity function. The pairwise similarity function r | STRING |
| HIGH | sentence_transformers/util/quantization.py | 31 | Performs semantic search using the FAISS library. Rescoring will be performed if: 1. `rescore` is True | STRING |
| HIGH | sentence_transformers/util/quantization.py | 215 | Performs semantic search using the usearch library. Rescoring will be performed if: 1. `rescore` is True | STRING |
| HIGH | sentence_transformers/util/tensor.py | 195 | Returns a new tensor with only the top-k values (in absolute terms) of each embedding, all others set to zero. | STRING |
| HIGH | sentence_transformers/util/file_io.py | 41 | Checks if the given model name or path corresponds to a SentenceTransformer model. Args: model_name_or | STRING |
| HIGH | sentence_transformers/util/file_io.py | 82 | Loads a file from a local or remote location. Args: model_name_or_path (str): The model name or path. | STRING |
| HIGH | sentence_transformers/util/file_io.py | 141 | Loads the subfolder path for a given model name or path. Args: model_name_or_path (str): The name or p | STRING |
| HIGH | sentence_transformers/util/file_io.py | 206 | Download a URL to a local file with a progress bar. The content is streamed in chunks and first written to a tempor | STRING |
| HIGH | sentence_transformers/backend/optimize.py | 27 | Export an optimized ONNX model from a SentenceTransformer, SparseEncoder, CrossEncoder, or MultiVectorEncoder m | STRING |
| HIGH | sentence_transformers/backend/quantize.py | 32 | Export a quantized ONNX model from a SentenceTransformer, SparseEncoder, CrossEncoder, or MultiVectorEncoder mo | STRING |
| HIGH | sentence_transformers/backend/quantize.py | 121 | Export a quantized OpenVINO model from a SentenceTransformer, SparseEncoder, CrossEncoder, or MultiVectorEncode | STRING |
| HIGH | sentence_transformers/cross_encoder/model.py | 584 | Performs predictions with the CrossEncoder on the given input pairs. .. tip:: Adjusting ` | STRING |
| HIGH | sentence_transformers/base/modality.py | 693 | Infer the modality of a single input sample by inspecting its type/structure. Pure type-based detection, does not r | STRING |
| HIGH | sentence_transformers/base/modules/module.py | 321 | A utility function to load the PyTorch weights of a model from a checkpoint. The checkpoint can be either a | STRING |
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| LOW | sentence_transformers/sentence_transformer/model.py | 754 | CODE | |
| LOW | sentence_transformers/sentence_transformer/model.py | 1133 | CODE | |
| LOW | sentence_transformers/sentence_transformer/model.py | 1198 | CODE | |
| LOW | sentence_transformers/sentence_transformer/fit_mixin.py | 62 | CODE | |
| LOW | sentence_transformers/sentence_transformer/fit_mixin.py | 398 | CODE | |
| LOW | sentence_transformers/sentence_transformer/fit_mixin.py | 456 | CODE | |
| LOW | …tence_transformer/losses/multiple_negatives_ranking.py | 237 | CODE | |
| LOW | …ransformer/losses/cached_multiple_negatives_ranking.py | 244 | CODE | |
| LOW | …transformers/sentence_transformer/losses/matryoshka.py | 88 | CODE | |
| LOW | …sformers/sentence_transformer/losses/adaptive_layer.py | 213 | CODE | |
| LOW | …rmers/sentence_transformer/losses/cached_gist_embed.py | 181 | CODE | |
| LOW | …tence_transformer/datasets/no_duplicates_dataloader.py | 29 | CODE | |
| LOW | …ntence_transformer/evaluation/information_retrieval.py | 308 | CODE | |
| LOW | …ntence_transformer/evaluation/information_retrieval.py | 477 | CODE | |
| LOW | …formers/sentence_transformer/evaluation/translation.py | 102 | CODE | |
| LOW | …ransformers/sentence_transformer/evaluation/triplet.py | 93 | CODE | |
| LOW | …s/sentence_transformer/evaluation/paraphrase_mining.py | 245 | CODE | |
| LOW | …e_transformers/sentence_transformer/modules/pooling.py | 171 | CODE | |
| LOW | …e_transformers/sentence_transformer/modules/pooling.py | 245 | CODE | |
| LOW | …rmers/sentence_transformer/modules/tokenizer/phrase.py | 63 | CODE | |
| LOW | sentence_transformers/util/hard_negatives.py | 25 | CODE | |
| LOW | sentence_transformers/util/deprecated_import.py | 212 | CODE | |
| LOW | sentence_transformers/util/quantization.py | 18 | CODE | |
| LOW | sentence_transformers/util/quantization.py | 202 | CODE | |
| LOW | sentence_transformers/util/quantization.py | 390 | CODE | |
| LOW | sentence_transformers/util/dataset.py | 159 | CODE | |
| LOW | sentence_transformers/util/retrieval.py | 89 | CODE | |
| LOW | sentence_transformers/util/retrieval.py | 167 | CODE | |
| LOW | sentence_transformers/util/retrieval.py | 258 | CODE | |
| LOW | sentence_transformers/util/environment.py | 56 | CODE | |
| LOW | sentence_transformers/util/file_io.py | 205 | CODE | |
| LOW | sentence_transformers/util/decorators.py | 97 | CODE | |
| LOW | sentence_transformers/util/decorators.py | 120 | CODE | |
| LOW | sentence_transformers/multi_vector_encoder/model.py | 718 | CODE | |
| LOW | sentence_transformers/backend/utils.py | 155 | CODE | |
| LOW | sentence_transformers/cross_encoder/model.py | 283 | CODE | |
| LOW | sentence_transformers/cross_encoder/model.py | 357 | CODE | |
| LOW | sentence_transformers/cross_encoder/fit_mixin.py | 59 | CODE | |
| LOW | sentence_transformers/cross_encoder/fit_mixin.py | 407 | CODE | |
| LOW | sentence_transformers/sparse_encoder/model.py | 940 | CODE | |
| LOW | …rs/sparse_encoder/evaluation/reciprocal_rank_fusion.py | 105 | CODE | |
| LOW | …_transformers/sparse_encoder/modules/splade_pooling.py | 66 | CODE | |
| LOW | sentence_transformers/base/model.py | 918 | CODE | |
| LOW | sentence_transformers/base/model.py | 1116 | CODE | |
| LOW | sentence_transformers/base/modality.py | 313 | CODE | |
| LOW | sentence_transformers/base/modality.py | 583 | CODE | |
| LOW | sentence_transformers/base/modality.py | 607 | CODE | |
| LOW | sentence_transformers/base/data_collator.py | 150 | CODE | |
| LOW | sentence_transformers/base/trainer.py | 399 | CODE | |
| LOW | sentence_transformers/base/trainer.py | 424 | CODE | |
| LOW | sentence_transformers/base/trainer.py | 543 | CODE | |
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| LOW | sentence_transformers/base/trainer.py | 1242 | CODE | |
| LOW | sentence_transformers/base/model_card.py | 429 | CODE | |
| LOW | sentence_transformers/base/model_card.py | 512 | CODE | |
| LOW | sentence_transformers/base/model_card.py | 614 | CODE | |
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| LOW | sentence_transformers/base/model_card.py | 1382 | CODE | |
| LOW | sentence_transformers/base/model_card.py | 1915 | CODE | |
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| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| MEDIUM | sentence_transformers/base/modules/router.py | 77 | # Use a regular SentenceTransformer for the document embeddings, and a static embedding model for the qu | COMMENT |
| MEDIUM | docs/migration_guide.md | 572 | # and a static embedding model for the query embeddings | COMMENT |
| MEDIUM | docs/migration_guide.md | 599 | # and a static embedding model for the query embeddings | COMMENT |
| MEDIUM | docs/cross_encoder/training_overview.md | 323 | # Mine hard negatives using a very efficient embedding model | COMMENT |
| MEDIUM | docs/cross_encoder/training_overview.md | 335 | batch_size=4096, # Use a batch size of 4096 for the embedding model | CODE |
| MEDIUM | docs/cross_encoder/training_overview.md | 607 | # Mine hard negatives using a very efficient embedding model | COMMENT |
| MEDIUM | docs/cross_encoder/training_overview.md | 614 | batch_size=4096, # Use a batch size of 4096 for the embedding model | CODE |
| MEDIUM | docs/cross_encoder/training_overview.md | 955 | # 2b. Modify our training dataset to include hard negatives using a very efficient embedding model | COMMENT |
| MEDIUM | docs/cross_encoder/training_overview.md | 965 | batch_size=4096, # Use a batch size of 4096 for the embedding model | CODE |
| MEDIUM | docs/cross_encoder/training_overview.md | 988 | # embedding model as a baseline. | COMMENT |
| MEDIUM | …nsformer/applications/embedding-quantization/README.md | 37 | # 1. Load an embedding model | COMMENT |
| MEDIUM | …nsformer/applications/embedding-quantization/README.md | 109 | # 1. Load an embedding model | COMMENT |
| MEDIUM | …rmer/applications/parallel-sentence-mining/bucc2018.py | 62 | # Use PCA to reduce the dimensionality of the sentence embedding model | COMMENT |
| MEDIUM | …cross_encoder/training/rerankers/training_gooaq_bce.py | 54 | # 2b. Modify our training dataset to include hard negatives using a very efficient embedding model | COMMENT |
| MEDIUM | …cross_encoder/training/rerankers/training_gooaq_bce.py | 64 | batch_size=4096, # Use a batch size of 4096 for the embedding model | CODE |
| MEDIUM | …cross_encoder/training/rerankers/training_gooaq_bce.py | 87 | # embedding model as a baseline. | COMMENT |
| MEDIUM | …ss_encoder/training/rerankers/training_gooaq_lambda.py | 54 | # 2b. Modify our training dataset to include hard negatives using a very efficient embedding model | COMMENT |
| MEDIUM | …ss_encoder/training/rerankers/training_gooaq_lambda.py | 64 | batch_size=4096, # Use a batch size of 4096 for the embedding model | CODE |
| MEDIUM | …ss_encoder/training/rerankers/training_gooaq_lambda.py | 86 | # embedding model as a baseline. | COMMENT |
| MEDIUM | …es/cross_encoder/training/rerankers/training_nq_bce.py | 54 | # 2b. Modify our training dataset to include hard negatives using a very efficient embedding model | COMMENT |
| MEDIUM | …es/cross_encoder/training/rerankers/training_nq_bce.py | 64 | batch_size=4096, # Use a batch size of 4096 for the embedding model | CODE |
| MEDIUM | …es/cross_encoder/training/rerankers/training_nq_bce.py | 87 | # embedding model as a baseline. | COMMENT |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW⚡ | …mers/sentence_transformer/losses/batch_hard_triplet.py | 243 | # Check if labels[i] == labels[j] | COMMENT |
| LOW⚡ | …mers/sentence_transformer/losses/batch_hard_triplet.py | 257 | # Check if labels[i] != labels[k] | COMMENT |
| LOW | sentence_transformers/util/tensor.py | 40 | # Check if list contains sparse tensors | COMMENT |
| LOW | sentence_transformers/util/retrieval.py | 332 | # Check if we need to increase sort_max_size | COMMENT |
| LOW | sentence_transformers/cross_encoder/model_card.py | 211 | # Check if any pair element is non-text (from usage_examples before asset saving) | COMMENT |
| LOW | …arse_encoder/evaluation/sparse_embedding_similarity.py | 78 | # Print the results | STRING |
| LOW | …rse_encoder/evaluation/sparse_information_retrieval.py | 128 | # Print the results | STRING |
| LOW | …ansformers/sparse_encoder/evaluation/sparse_triplet.py | 90 | # Print the results | STRING |
| LOW | …sformers/sparse_encoder/evaluation/sparse_nano_beir.py | 171 | # Print the results | STRING |
| LOW | …rse_encoder/evaluation/sparse_binary_classification.py | 104 | # Print the results | STRING |
| LOW | …e_transformers/sparse_encoder/evaluation/sparse_mse.py | 78 | # Print the results | STRING |
| LOW | …ormers/sparse_encoder/evaluation/sparse_translation.py | 75 | # Print the results | STRING |
| LOW | …sformers/sparse_encoder/evaluation/sparse_reranking.py | 102 | # Print the results | STRING |
| LOW | …mers/sparse_encoder/modules/sparse_static_embedding.py | 202 | # Check if we have a JSON path in config | COMMENT |
| LOW | sentence_transformers/base/model.py | 1059 | # Check if this is a Sentence Transformer model | COMMENT |
| LOW | sentence_transformers/base/model.py | 1145 | # Check if the config_sentence_transformers.json file exists (exists since v2 of the framework) | COMMENT |
| LOW | sentence_transformers/base/model.py | 1173 | # Check if a readme exists. README is optional metadata. A transient Hub error | COMMENT |
| LOW | sentence_transformers/base/model.py | 1227 | # Check if the `load` method only accepts a single parameter (the path to the local directory). | COMMENT |
| LOW | sentence_transformers/base/model_card.py | 671 | # Check if the model has a tuple modality whose parts all match available columns. | STRING |
| LOW | tests/sparse_encoder/utils.py | 26 | # Check if shape matches | COMMENT |
| LOW | tests/sparse_encoder/utils.py | 40 | # Check if indices are the same | COMMENT |
| LOW | tests/sparse_encoder/utils.py | 44 | # Check if values are close | COMMENT |
| LOW | tests/sparse_encoder/test_trainer.py | 116 | # Check if model parameters have changed after training | COMMENT |
| LOW | …sparse_encoder/modules/test_sparse_static_embedding.py | 55 | # Check if embeddings are the same before and after save/load | COMMENT |
| LOW | …sparse_encoder/modules/test_sparse_static_embedding.py | 58 | # Check if SparseStaticEmbedding weights are maintained after loading | COMMENT |
| LOW | …modules/transformer/update_transformers_tiny_models.py | 42 | # Check if the model_id contains the architecture name | COMMENT |
| LOW | …er/training/quora_duplicate_questions/create_splits.py | 510 | ####### Write files for Information Retrieval ##### | COMMENT |
| LOW | …/embedding-quantization/semantic_search_recommended.py | 120 | # Output the results | COMMENT |
| LOW | …tions/semantic-search/semantic_search_quora_pytorch.py | 34 | # Check if embedding cache path exists | COMMENT |
| LOW | …cations/semantic-search/semantic_search_quora_annoy.py | 55 | # Check if embedding cache path exists | COMMENT |
| LOW | …tions/semantic-search/semantic_search_quora_hnswlib.py | 45 | # Check if embedding cache path exists | COMMENT |
| LOW | …cations/semantic-search/semantic_search_quora_faiss.py | 59 | # Check if embedding cache path exists | COMMENT |
| LOW | …/cross_encoder/applications/cross_encoder_reranking.py | 41 | # Check if embedding cache path exists | COMMENT |
| LOW | …parse_encoder/evaluation/sparse_reranking_evaluator.py | 53 | # Print the results | COMMENT |
| LOW | …coder/evaluation/sparse_nanobeir_advanced_evaluator.py | 45 | # Print the results | COMMENT |
| LOW | …arse_encoder/evaluation/sparse_similarity_evaluator.py | 31 | # Print the results | COMMENT |
| LOW | …rse_encoder/evaluation/sparse_translation_evaluator.py | 30 | # Print the results | COMMENT |
| LOW | …/sparse_encoder/evaluation/sparse_triplet_evaluator.py | 37 | # Print the results | COMMENT |
| LOW | …parse_encoder/evaluation/sparse_retrieval_evaluator.py | 69 | # Print the results | COMMENT |
| LOW | …sparse_encoder/evaluation/sparse_nanobeir_evaluator.py | 94 | # Print the results | COMMENT |
| LOW | …_encoder/evaluation/sparse_classification_evaluator.py | 58 | # Print the results | COMMENT |
| LOW | …ples/sparse_encoder/evaluation/sparse_mse_evaluator.py | 32 | # Print the results | COMMENT |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | …transformers/sentence_transformer/losses/gist_embed.py | 245 | # so the label for anchor[i] is i. This means that we can just use arange | COMMENT |
| LOW | …rmers/sentence_transformer/losses/cached_gist_embed.py | 281 | # so the label for anchor[i] is i. This means that we can just use arange | COMMENT |
| LOW | sentence_transformers/base/trainer.py | 525 | # if loss is nan or inf simply add the average of previous logged losses | COMMENT |
| LOW | sentence_transformers/base/trainer.py | 578 | # would not accept it. If None, we just call the super().log() method without it so that it works with all versi | COMMENT |
| LOW | sentence_transformers/base/trainer.py | 654 | # If the evaluator is not defined, we can just return the output | COMMENT |
| MEDIUM | …applications/parallel-sentence-mining/bitext_mining.py | 27 | # Model we want to use for bitext mining. sentence-transformers/LaBSE achieves state-of-the-art performance | COMMENT |
| MEDIUM | …rmer/applications/parallel-sentence-mining/bucc2018.py | 25 | # Model we want to use for bitext mining. sentence-transformers/LaBSE achieves state-of-the-art performance | COMMENT |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | …raining/data_augmentation/train_sts_qqp_crossdomain.py | 102 | # Step 1: Train cross-encoder model with STSbenchmark | COMMENT |
| LOW | …raining/data_augmentation/train_sts_qqp_crossdomain.py | 141 | # Step 2: Label QQP train dataset using cross-encoder (BERT) model | COMMENT |
| LOW | …raining/data_augmentation/train_sts_qqp_crossdomain.py | 158 | # Step 3: Train bi-encoder (SBERT) model with QQP dataset - Augmented SBERT | COMMENT |
| LOW | …/training/data_augmentation/train_sts_indomain_bm25.py | 89 | # Step 1: Train cross-encoder model with (gold) STS benchmark dataset | COMMENT |
| LOW | …/training/data_augmentation/train_sts_indomain_bm25.py | 135 | # Step 2: Label BM25 sampled STSb (silver dataset) using cross-encoder model | COMMENT |
| LOW | …/training/data_augmentation/train_sts_indomain_bm25.py | 188 | # Step 3: Train bi-encoder model with both (gold + silver) STSbenchmark dataset - Augmented SBERT | COMMENT |
| LOW | …ining/data_augmentation/train_sts_indomain_semantic.py | 101 | # Step 1: Train cross-encoder model with STSbenchmark | COMMENT |
| LOW | …ining/data_augmentation/train_sts_indomain_semantic.py | 140 | # Step 2: Find silver pairs to label | COMMENT |
| LOW | …ining/data_augmentation/train_sts_indomain_semantic.py | 199 | # Step 3: Train bi-encoder model with both STSbenchmark and labeled AllNlI - Augmented SBERT | COMMENT |
| Severity | File | Line | Snippet | Context |
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| LOW | …raining/data_augmentation/train_sts_qqp_crossdomain.py | 102 | # Step 1: Train cross-encoder model with STSbenchmark | COMMENT |
| LOW | …raining/data_augmentation/train_sts_qqp_crossdomain.py | 141 | # Step 2: Label QQP train dataset using cross-encoder (BERT) model | COMMENT |
| LOW | …raining/data_augmentation/train_sts_qqp_crossdomain.py | 158 | # Step 3: Train bi-encoder (SBERT) model with QQP dataset - Augmented SBERT | COMMENT |
| LOW | …/training/data_augmentation/train_sts_indomain_bm25.py | 89 | # Step 1: Train cross-encoder model with (gold) STS benchmark dataset | COMMENT |
| LOW | …/training/data_augmentation/train_sts_indomain_bm25.py | 135 | # Step 2: Label BM25 sampled STSb (silver dataset) using cross-encoder model | COMMENT |
| LOW | …/training/data_augmentation/train_sts_indomain_bm25.py | 188 | # Step 3: Train bi-encoder model with both (gold + silver) STSbenchmark dataset - Augmented SBERT | COMMENT |
| LOW | …ining/data_augmentation/train_sts_indomain_semantic.py | 101 | # Step 1: Train cross-encoder model with STSbenchmark | COMMENT |
| LOW | …ining/data_augmentation/train_sts_indomain_semantic.py | 140 | # Step 2: Find silver pairs to label | COMMENT |
| LOW | …ining/data_augmentation/train_sts_indomain_semantic.py | 199 | # Step 3: Train bi-encoder model with both STSbenchmark and labeled AllNlI - Augmented SBERT | COMMENT |
| Severity | File | Line | Snippet | Context |
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| LOW | docs/conf.py | 1 | # Configuration file for the Sphinx documentation builder. | COMMENT |
| LOW | docs/sparse_encoder/training_overview.md | 141 | router = Router.for_query_document( | COMMENT |
| LOW | …raining/data_augmentation/train_sts_qqp_crossdomain.py | 141 | # Step 2: Label QQP train dataset using cross-encoder (BERT) model | COMMENT |
| LOW | …ransformer/training/distillation/model_quantization.py | 61 | }, | COMMENT |
| LOW | …transformer/training/multilingual/make_multilingual.py | 81 | # If we want, we can limit the maximum sequence length for the model | COMMENT |
| LOW | …ations/embedding-quantization/semantic_search_faiss.py | 61 | # In the first call we'll provide the `corpus_embeddings` and get the `corpus_index` back, which | COMMENT |
| LOW | …ions/embedding-quantization/semantic_search_usearch.py | 61 | # In the first call we'll provide the `corpus_embeddings` and get the `corpus_index` back, which | COMMENT |
| LOW | …ples/multi_vector_encoder/compression/token_pooling.py | 61 | # (3) Pipeline module: bake the pooling into the model so every consumer of the saved | COMMENT |
| LOW | …ncoder/training/ms_marco/training_ms_marco_plistmle.py | 101 | # lambda_weight = PListMLELambdaWeight(rank_discount_fn=custom_discount) | COMMENT |
| LOW | examples/cross_encoder/training/distillation/README.md | 61 | # {"corpus_id": 4, "score": 0.91639173}, | COMMENT |
| LOW | .github/workflows/sync-skills.yml | 1 | name: Sync skill to huggingface/skills | COMMENT |
| LOW | …train_sentence_transformer_static_embedding_example.py | 1 | #!/usr/bin/env python3 | COMMENT |
| LOW | …cripts/train_sentence_transformer_with_lora_example.py | 1 | #!/usr/bin/env python3 | COMMENT |
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| HIGH | …ormers/multi_vector_encoder/evaluation/distillation.py | 214 | # One document per query: a per-query distribution is undefined, so this softmaxes over | COMMENT |
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| MEDIUM | …te_questions/application_duplicate_questions_mining.py | 10 | # For demonstration purposes, we limit it to a few questions which all have on duplicate | COMMENT |
| MEDIUM | …ce_transformer/training/prompts/training_nq_prompts.py | 26 | # Feel free to adjust these variables: | COMMENT |
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| LOW⚡ | tests/base/test_model.py | 382 | SentenceTransformer(modules=[torch.nn.Linear(10, 10)], use_auth_token="fake-token") | CODE |