State-of-the-Art Embeddings, Retrieval, and Reranking
This report summarizes scanner evidence for huggingface/sentence-transformers, a Python project with 19,160 GitHub stars. SynthScan examined 143,461 lines of code across 701 source files, recording 3370 pattern matches distributed across 19 syntactic categories. The legacy adjusted pattern signal is 28.7 (Elevated pattern signal); it is not an authorship estimate.
Use the findings to ask whether maintainers can explain intent, verify dependencies, simplify unnecessary scaffolding, and focus review effort. Risk Payload v1 adds ownership, supply-chain, over-engineering, and review-effort vectors when available. These are prompts for human verification, not probabilities, security findings, or measured maintenance costs.
Use each vector to guide manual verification of the underlying findings.
Can maintainers explain intent, assumptions, and trade-offs? 2188 finding(s).
Are imports declared and package/API names verified? 699 finding(s).
Are wrappers, guards, and repeated structures necessary? 2353 finding(s).
Which changes need focused human review? 3370 finding(s).
This chart tracks the legacy adjusted pattern score across scan runs. Changes can reflect source changes, scan coverage, or detector/scoring versions; they do not measure AI adoption or prove that a team refactored code. Compare runs only when their scoring versions are compatible. The dashed line tracks the raw pattern-hit count.
Groups findings by configured severity weight. Severity is not a confidence estimate, security rating, or proof that a package is missing. Verify imports against the project's manifests and inspect each finding in its local design context.
This horizontal bar chart shows where configured pattern scores accumulate across directories. Treat the bars as locations to inspect, not as evidence of authorship or code quality. Use the file-level findings to decide whether a directory needs a focused ownership, dependency, complexity, or review check.
The scanner identified 3370 distinct pattern matches across 19 syntactic categories. Each entry below represents a discrete location in the source code where the engine recorded a detector match to inspect in context. 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 configured finding weight (CRITICAL / HIGH / MEDIUM / LOW). The Context column identifies whether the match occurred inside executable code, an inline comment, or a string literal — legacy comment-context matches receive a ×1.5 score weight. The ⚡ bolt icon marks clustered matches: three or more patterns within a 10-line window, each receiving an additional ×1.5 density multiplier under the legacy scoring model.
| 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 | 1134 | def set_pooling_include_prompt(self, include_prompt: bool) -> None: | CODE |
| LOW | sentence_transformers/sentence_transformer/model.py | 1180 | def get_sentence_embedding_dimension(self) -> int | None: | CODE |
| LOW | sentence_transformers/sentence_transformer/model.py | 1218 | 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 | 91 | 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 | 228 | def get_anchor_positive_triplet_mask(labels: Tensor) -> Tensor: | CODE |
| LOW⚡ | …mers/sentence_transformer/losses/batch_hard_triplet.py | 247 | def get_anchor_negative_triplet_mask(labels: Tensor) -> Tensor: | CODE |
| LOW | …transformers/sentence_transformer/losses/margin_mse.py | 179 | 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 | 246 | 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 | 482 | def _load_dataset_subset_split(self, subset: str, split: str, required_columns: list[str]): | CODE |
| LOW | …nsformers/sentence_transformer/evaluation/nano_beir.py | 537 | 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 | 508 | def _fill_empty_document_scores(scores: Tensor, empty: Tensor) -> Tensor: | CODE |
| LOW | sentence_transformers/util/similarity.py | 882 | def to_similarity_pairwise_fn( | CODE |
| LOW | sentence_transformers/util/tensor.py | 273 | def stack_padded_token_embeddings(embeddings: list[Tensor], masks: list[Tensor]) -> tuple[Tensor, Tensor]: | CODE |
| LOW | sentence_transformers/util/tensor.py | 293 | 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 | 113 | 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 | 216 | 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 | 1175 | 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 | 1181 | def _get_module_init_defaults(self, class_ref: str) -> dict[str, Any]: | CODE |
| LOW | sentence_transformers/multi_vector_encoder/model.py | 1334 | def _build_stanford_projection( | CODE |
| LOW | sentence_transformers/multi_vector_encoder/model.py | 1367 | 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 |
| 1692 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 | |
| 637 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/model.py | 0 | run local inference on normalized inputs with resolved arguments. | STRING |
| HIGH | sentence_transformers/multi_vector_encoder/model.py | 0 | run local inference on normalized inputs with resolved arguments. | STRING |
| HIGH | sentence_transformers/cross_encoder/model.py | 0 | run local inference on normalized inputs with resolved arguments. | STRING |
| HIGH | sentence_transformers/sparse_encoder/model.py | 0 | run local inference on normalized inputs with resolved arguments. | STRING |
| HIGH | sentence_transformers/base/model.py | 0 | run local inference on normalized inputs with resolved arguments. | 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 |
| 60 more matches not shown… | ||||
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| MEDIUM | …transformers/sentence_transformer/losses/gist_embed.py | 194 | # Define the anchor threshold | COMMENT |
| MEDIUM | …rmers/sentence_transformer/losses/cached_gist_embed.py | 300 | # 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 | 167 | """This function is deprecated. Use semantic_search instead""" | STRING |
| 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 | 226 | # 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/base/sampler.py | 624 | # Create a random numpy permutation using int32 (or int64 if necessary) | COMMENT |
| MEDIUM | sentence_transformers/base/modules/transformer.py | 2797 | # 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 | 137 | # Create a pool | COMMENT |
| MEDIUM | tests/sentence_transformer/test_multi_process.py | 234 | # Create a pool | COMMENT |
| MEDIUM | tests/sentence_transformer/test_trainer.py | 205 | # Create a new model card if a Trainer was initialized | COMMENT |
| MEDIUM | tests/sentence_transformer/test_trainer.py | 868 | # Define a custom batch sampler function | COMMENT |
| MEDIUM | tests/sentence_transformer/test_trainer.py | 998 | # Define a custom multi-dataset batch sampler function | COMMENT |
| MEDIUM | tests/util/test_hard_negatives.py | 1441 | # Create a larger dataset with 32 entries | COMMENT |
| MEDIUM | tests/util/test_hard_negatives.py | 1556 | # Create a dataset with just 2 pairs | COMMENT |
| MEDIUM⚡ | tests/cross_encoder/test_model.py | 835 | # 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 | 485 | # 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 | 13 | # Create a wrapper to measure outputs of the forward method | COMMENT |
| MEDIUM | tests/sparse_encoder/modules/test_csr.py | 63 | # Create the wrapper and replace the forward method | COMMENT |
| MEDIUM | tests/base/samplers/test_no_duplicates_batch_sampler.py | 53 | # 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 |
| MEDIUM | …ing/distillation/model_distillation_layer_reduction.py | 212 | # Create the trainer & start training | COMMENT |
| MEDIUM | …ransformer/training/distillation/model_quantization.py | 106 | # Create a function to evaluate the models | COMMENT |
| MEDIUM | …ining/avg_word_embeddings/training_stsbenchmark_bow.py | 48 | # Create the vocab for the BoW model | COMMENT |
| 31 more matches not shown… | ||||
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| HIGH | sentence_transformers/sentence_transformer/fit_mixin.py | 279 | except TypeError: | CODE |
| HIGH | sentence_transformers/util/hard_negatives.py | 525 | except Exception: | CODE |
| HIGH | sentence_transformers/util/misc.py | 244 | except (OSError, ValueError): | CODE |
| HIGH | sentence_transformers/util/tensor.py | 22 | except TypeError: | CODE |
| HIGH | sentence_transformers/backend/optimize.py | 15 | except ImportError: | CODE |
| HIGH | sentence_transformers/backend/quantize.py | 16 | except ImportError: | CODE |
| HIGH | sentence_transformers/backend/quantize.py | 20 | except ImportError: | CODE |
| HIGH | sentence_transformers/cross_encoder/model.py | 772 | except ImportError: | CODE |
| HIGH | sentence_transformers/cross_encoder/fit_mixin.py | 283 | except TypeError: | CODE |
| HIGH | sentence_transformers/sparse_encoder/search_engines.py | 18 | except ImportError: | CODE |
| HIGH | sentence_transformers/sparse_encoder/search_engines.py | 22 | except ImportError: | CODE |
| HIGH | sentence_transformers/sparse_encoder/search_engines.py | 26 | except ImportError: | CODE |
| HIGH | sentence_transformers/sparse_encoder/search_engines.py | 30 | except ImportError: | CODE |
| HIGH | sentence_transformers/base/training_args.py | 167 | except json.JSONDecodeError: | CODE |
| HIGH | sentence_transformers/base/model.py | 559 | except ImportError: | CODE |
| HIGH | sentence_transformers/base/model.py | 630 | except (ValueError, TypeError): | CODE |
| HIGH | sentence_transformers/base/model.py | 685 | except ImportError: | CODE |
| HIGH | sentence_transformers/base/model.py | 1206 | except Exception: | CODE |
| HIGH | sentence_transformers/base/trainer.py | 707 | except Exception: | CODE |
| HIGH | sentence_transformers/base/model_card.py | 495 | except Exception: | CODE |
| HIGH | sentence_transformers/base/model_card.py | 872 | except Exception: | CODE |
| HIGH | sentence_transformers/base/model_card.py | 915 | except Exception: | CODE |
| HIGH | sentence_transformers/base/model_card.py | 1618 | except Exception: | CODE |
| HIGH | sentence_transformers/base/model_card.py | 1873 | except Exception: | CODE |
| HIGH | sentence_transformers/base/model_card.py | 1930 | except Exception: | CODE |
| HIGH | sentence_transformers/base/model_card.py | 1418 | except Exception: | CODE |
| HIGH | sentence_transformers/base/model_card.py | 1458 | except Exception: | CODE |
| HIGH | sentence_transformers/base/evaluation/evaluator.py | 88 | except ValueError: | CODE |
| HIGH | sentence_transformers/base/modules/transformer.py | 210 | except ImportError: | CODE |
| HIGH | sentence_transformers/base/modules/transformer.py | 168 | except (ImportError, KeyError): | CODE |
| HIGH | sentence_transformers/base/modules/transformer.py | 2146 | except (KeyError, RuntimeError): | CODE |
| HIGH | sentence_transformers/base/modules/router.py | 623 | except (ValueError, TypeError): | CODE |
| HIGH | tests/cross_encoder/test_model_card.py | 290 | except Exception: | CODE |
| HIGH | tests/base/modules/test_transformer.py | 2440 | except RuntimeError: | CODE |
| HIGH | …transformer/training/multilingual/make_multilingual.py | 182 | except Exception: | CODE |
| HIGH | …pts/train_sentence_transformer_distillation_example.py | 112 | except Exception: | CODE |
| HIGH | …ts/train_sentence_transformer_multi_dataset_example.py | 116 | except Exception: | CODE |
| HIGH | …sformers/scripts/train_sentence_transformer_example.py | 87 | except Exception: | CODE |
| HIGH | …ce-transformers/scripts/train_cross_encoder_example.py | 74 | except Exception: | CODE |
| HIGH | …ripts/train_sentence_transformer_matryoshka_example.py | 68 | except Exception: | CODE |
| HIGH | …ormers/scripts/train_cross_encoder_listwise_example.py | 91 | except Exception: | CODE |
| HIGH | …e-transformers/scripts/train_sparse_encoder_example.py | 74 | except Exception: | CODE |
| HIGH | …sformers/scripts/train_multi_vector_encoder_example.py | 87 | except Exception: | CODE |
| HIGH | …rain_sentence_transformer_make_multilingual_example.py | 82 | except Exception: | CODE |
| HIGH | …train_sentence_transformer_static_embedding_example.py | 97 | except Exception: | CODE |
| HIGH | …s/scripts/train_sparse_encoder_distillation_example.py | 77 | except Exception: | CODE |
| HIGH | …cripts/train_sentence_transformer_with_lora_example.py | 130 | except Exception: | CODE |
| HIGH | …rs/scripts/train_cross_encoder_distillation_example.py | 83 | except Exception: | CODE |
| 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 | 491 | except Exception as e: | CODE |
| LOW | sentence_transformers/util/hard_negatives.py | 525 | 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/distributed.py | 142 | except Exception: | CODE |
| LOW | sentence_transformers/util/distributed.py | 175 | except Exception: | CODE |
| LOW | sentence_transformers/util/environment.py | 192 | except Exception as e: | CODE |
| LOW⚡ | sentence_transformers/util/file_io.py | 100 | except Exception: | CODE |
| LOW⚡ | sentence_transformers/util/file_io.py | 105 | except Exception as exc: | CODE |
| LOW | sentence_transformers/util/file_io.py | 276 | except Exception as first_error: | CODE |
| LOW | sentence_transformers/util/file_io.py | 339 | except Exception: | CODE |
| LOW | …nce_transformers/cross_encoder/evaluation/nano_beir.py | 398 | except Exception as e: | CODE |
| LOW | sentence_transformers/base/model.py | 852 | except Exception: | CODE |
| LOW | sentence_transformers/base/model.py | 1020 | except Exception as exc: | CODE |
| LOW | sentence_transformers/base/model.py | 1196 | except Exception as exc: | CODE |
| LOW | sentence_transformers/base/model.py | 1206 | except Exception: | CODE |
| LOW | sentence_transformers/base/model.py | 1833 | except Exception as exc: | CODE |
| LOW | sentence_transformers/base/model.py | 1850 | except Exception: | CODE |
| LOW | sentence_transformers/base/modality.py | 492 | except Exception as exc: | CODE |
| LOW | sentence_transformers/base/trainer.py | 707 | except Exception: | CODE |
| LOW | sentence_transformers/base/trainer.py | 712 | except Exception as exc: | CODE |
| LOW | sentence_transformers/base/sampler.py | 382 | except Exception: | CODE |
| MEDIUM | sentence_transformers/base/model_card.py | 1455 | def try_to_float(metric_value): | CODE |
| LOW⚡ | sentence_transformers/base/model_card.py | 1349 | except Exception: | CODE |
| LOW⚡ | sentence_transformers/base/model_card.py | 1973 | except Exception as exc: | CODE |
| LOW⚡ | sentence_transformers/base/model_card.py | 1981 | except Exception as exc: | CODE |
| LOW⚡ | sentence_transformers/base/model_card.py | 1989 | except Exception as exc: | CODE |
| LOW⚡ | sentence_transformers/base/model_card.py | 1996 | except Exception as exc: | CODE |
| LOW | sentence_transformers/base/model_card.py | 456 | except Exception: | CODE |
| LOW | sentence_transformers/base/model_card.py | 872 | except Exception: | CODE |
| LOW | sentence_transformers/base/model_card.py | 915 | except Exception: | CODE |
| LOW | sentence_transformers/base/model_card.py | 1418 | except Exception: | CODE |
| LOW | sentence_transformers/base/model_card.py | 1458 | except Exception: | CODE |
| LOW | sentence_transformers/base/model_card.py | 1588 | except Exception: | CODE |
| LOW | sentence_transformers/base/model_card.py | 1596 | except Exception: | CODE |
| LOW | sentence_transformers/base/model_card.py | 1618 | except Exception: | CODE |
| LOW | sentence_transformers/base/model_card.py | 1873 | except Exception: | CODE |
| LOW | sentence_transformers/base/model_card.py | 1930 | except Exception: | CODE |
| LOW | sentence_transformers/base/model_card.py | 1941 | except Exception as exc: | CODE |
| LOW | sentence_transformers/base/model_card.py | 1947 | except Exception as exc: | CODE |
| LOW | sentence_transformers/base/model_card.py | 1958 | 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 | 2099 | except Exception as exc: | CODE |
| LOW | sentence_transformers/base/modules/transformer.py | 2137 | except Exception as exc: # never let suffix derivation break tokenization | CODE |
| LOW | sentence_transformers/base/modules/transformer.py | 2588 | 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 | 912 | 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 | 1134 | 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 | 15 | 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 | 24 | 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 | 12 | logger = logging.getLogger(__name__) | CODE |
| LOW | sentence_transformers/util/__init__.py | 70 | __all__ = [ | CODE |
| LOW | sentence_transformers/util/retrieval.py | 16 | logger = logging.getLogger(__name__) | CODE |
| LOW | sentence_transformers/util/environment.py | 18 | logger = logging.getLogger(__name__) | CODE |
| LOW | sentence_transformers/util/file_io.py | 42 | 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 |
| 61 more matches not shown… | ||||
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | sentence_transformers/sentence_transformer/model.py | 153 | CODE | |
| LOW | sentence_transformers/sentence_transformer/model.py | 226 | CODE | |
| LOW | sentence_transformers/sentence_transformer/model.py | 246 | 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 | 202 | Creates a StaticEmbedding instance from a distillation process using the `model2vec` package. Args: | STRING |
| HIGH | …rmers/sentence_transformer/modules/static_embedding.py | 268 | 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 | 843 | Converts a similarity function name or enum value to the corresponding similarity function. Args: | STRING |
| HIGH | sentence_transformers/util/similarity.py | 885 | Converts a similarity function into a pairwise similarity function. The pairwise similarity function r | STRING |
| HIGH | sentence_transformers/util/quantization.py | 33 | Performs semantic search using the FAISS library. Rescoring will be performed if: 1. `rescore` is True | STRING |
| HIGH | sentence_transformers/util/quantization.py | 217 | Performs semantic search using the usearch library. Rescoring will be performed if: 1. `rescore` is True | STRING |
| HIGH | sentence_transformers/util/tensor.py | 204 | 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 | 120 | 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 | 161 | 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 | 220 | 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 | 302 | 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 | 465 | Performs predictions with the CrossEncoder on the given input pairs. .. tip:: Adjusting ` | STRING |
| HIGH | sentence_transformers/base/modality.py | 726 | 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/fit_mixin.py | 62 | CODE | |
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| LOW | …tence_transformer/losses/multiple_negatives_ranking.py | 237 | CODE | |
| LOW | …transformers/sentence_transformer/losses/gist_embed.py | 140 | 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 | |
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| LOW | …rs/sparse_encoder/evaluation/reciprocal_rank_fusion.py | 105 | CODE | |
| LOW | …_transformers/sparse_encoder/modules/splade_pooling.py | 66 | 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 | 240 | # Check if labels[i] == labels[j] | COMMENT |
| LOW⚡ | …mers/sentence_transformer/losses/batch_hard_triplet.py | 254 | # 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 | 329 | # 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 | 1071 | # Check if this is a Sentence Transformer model | COMMENT |
| LOW | sentence_transformers/base/model.py | 1157 | # Check if the config_sentence_transformers.json file exists (exists since v2 of the framework) | COMMENT |
| LOW | sentence_transformers/base/model.py | 1185 | # Check if a readme exists. README is optional metadata. A transient Hub error | COMMENT |
| LOW | sentence_transformers/base/model.py | 1239 | # 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 | 41 | # 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 | 244 | # 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 | 283 | # so the label for anchor[i] is i. This means that we can just use arange | COMMENT |
| LOW | sentence_transformers/base/trainer.py | 529 | # if loss is nan or inf simply add the average of previous logged losses | COMMENT |
| LOW | sentence_transformers/base/trainer.py | 582 | # 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 | 658 | # 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 |
|---|---|---|---|---|
| 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 |
|---|---|---|---|---|
| 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 |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| HIGH | …ormers/multi_vector_encoder/evaluation/distillation.py | 214 | # One document per query: a per-query distribution is undefined, so this softmaxes over | COMMENT |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| 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 |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW⚡ | tests/base/test_model.py | 402 | SentenceTransformer(modules=[torch.nn.Linear(10, 10)], use_auth_token="fake-token") | CODE |