| | --- |
| | tags: |
| | - sentence-transformers |
| | - sentence-similarity |
| | - feature-extraction |
| | - dense |
| | - generated_from_trainer |
| | - dataset_size:42459 |
| | - loss:TripletLoss |
| | base_model: sentence-transformers/all-MiniLM-L6-v2 |
| | widget: |
| | - source_sentence: policy for how can i verify if a tekton task version is still supported |
| | by checking for the build.appstudio.redhat.com/expires-on annotation? |
| | sentences: |
| | - 'Helper: lib.to_array |
| | |
| | Signature: to_array(s) |
| | |
| | Description: ' |
| | - 'Helper: lib.pipelinerun_attestations |
| | |
| | Signature: pipelinerun_attestations |
| | |
| | Description: ' |
| | - 'Helper: lib.k8s.name |
| | |
| | Signature: name(resource) |
| | |
| | Description: ' |
| | - source_sentence: how to check attestation is missing statement field. |
| | sentences: |
| | - 'Helper: lib.k8s.name |
| | |
| | Signature: name(resource) |
| | |
| | Description: ' |
| | - 'Helper: lib.tekton.untrusted_task_refs |
| | |
| | Signature: untrusted_task_refs(tasks) |
| | |
| | Description: ' |
| | - 'Helper: lib.k8s.version |
| | |
| | Signature: version(resource) |
| | |
| | Description: ' |
| | - source_sentence: I need to ensure the operators.openshift.io/valid-subscription |
| | annotation in the ClusterServiceVersion manifest contains a valid JSON encoded |
| | non-empty array of strings. |
| | sentences: |
| | - 'Helper: lib.to_array |
| | |
| | Signature: to_array(s) |
| | |
| | Description: ' |
| | - 'Helper: lib.image.equal_ref |
| | |
| | Signature: equal_ref(ref1, ref2) |
| | |
| | Description: ' |
| | - 'Helper: lib.result_helper |
| | |
| | Signature: result_helper(chain, failure_sprintf_params) |
| | |
| | Description: ' |
| | - source_sentence: write a rule to deny approval for an container image with non-unique |
| | RPM names |
| | sentences: |
| | - 'Helper: lib.result_helper |
| | |
| | Signature: result_helper(chain, failure_sprintf_params) |
| | |
| | Description: ' |
| | - 'Helper: lib.to_set |
| | |
| | Signature: to_set(arr) |
| | |
| | Description: ' |
| | - 'Helper: lib.rule_data_defaults |
| | |
| | Signature: rule_data_defaults |
| | |
| | Description: ' |
| | - source_sentence: check if i need to validate that spdx package is an operating system |
| | component. |
| | sentences: |
| | - 'Helper: lib.to_set |
| | |
| | Signature: to_set(arr) |
| | |
| | Description: ' |
| | - 'Helper: lib.rule_data_defaults |
| | |
| | Signature: rule_data_defaults |
| | |
| | Description: ' |
| | - 'Helper: lib.result_helper |
| | |
| | Signature: result_helper(chain, failure_sprintf_params) |
| | |
| | Description: ' |
| | pipeline_tag: sentence-similarity |
| | library_name: sentence-transformers |
| | metrics: |
| | - cosine_accuracy |
| | model-index: |
| | - name: SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2 |
| | results: |
| | - task: |
| | type: triplet |
| | name: Triplet |
| | dataset: |
| | name: retrieval eval |
| | type: retrieval-eval |
| | metrics: |
| | - type: cosine_accuracy |
| | value: 0.9834675788879395 |
| | name: Cosine Accuracy |
| | --- |
| | |
| | # SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2 |
| |
|
| | This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2). It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. |
| |
|
| | ## Model Details |
| |
|
| | ### Model Description |
| | - **Model Type:** Sentence Transformer |
| | - **Base model:** [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) <!-- at revision c9745ed1d9f207416be6d2e6f8de32d1f16199bf --> |
| | - **Maximum Sequence Length:** 256 tokens |
| | - **Output Dimensionality:** 384 dimensions |
| | - **Similarity Function:** Cosine Similarity |
| | <!-- - **Training Dataset:** Unknown --> |
| | <!-- - **Language:** Unknown --> |
| | <!-- - **License:** Unknown --> |
| |
|
| | ### Model Sources |
| |
|
| | - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) |
| | - **Repository:** [Sentence Transformers on GitHub](https://github.com/huggingface/sentence-transformers) |
| | - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) |
| |
|
| | ### Full Model Architecture |
| |
|
| | ``` |
| | SentenceTransformer( |
| | (0): Transformer({'max_seq_length': 256, 'do_lower_case': False, 'architecture': 'BertModel'}) |
| | (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) |
| | (2): Normalize() |
| | ) |
| | ``` |
| |
|
| | ## Usage |
| |
|
| | ### Direct Usage (Sentence Transformers) |
| |
|
| | First install the Sentence Transformers library: |
| |
|
| | ```bash |
| | pip install -U sentence-transformers |
| | ``` |
| |
|
| | Then you can load this model and run inference. |
| | ```python |
| | from sentence_transformers import SentenceTransformer |
| | |
| | # Download from the 🤗 Hub |
| | model = SentenceTransformer("sentence_transformers_model_id") |
| | # Run inference |
| | sentences = [ |
| | 'check if i need to validate that spdx package is an operating system component.', |
| | 'Helper: lib.result_helper\nSignature: result_helper(chain, failure_sprintf_params)\nDescription: ', |
| | 'Helper: lib.to_set\nSignature: to_set(arr)\nDescription: ', |
| | ] |
| | embeddings = model.encode(sentences) |
| | print(embeddings.shape) |
| | # [3, 384] |
| | |
| | # Get the similarity scores for the embeddings |
| | similarities = model.similarity(embeddings, embeddings) |
| | print(similarities) |
| | # tensor([[ 1.0000, 0.4979, -0.4443], |
| | # [ 0.4979, 1.0000, -0.4918], |
| | # [-0.4443, -0.4918, 1.0000]]) |
| | ``` |
| |
|
| | <!-- |
| | ### Direct Usage (Transformers) |
| |
|
| | <details><summary>Click to see the direct usage in Transformers</summary> |
| |
|
| | </details> |
| | --> |
| |
|
| | <!-- |
| | ### Downstream Usage (Sentence Transformers) |
| |
|
| | You can finetune this model on your own dataset. |
| |
|
| | <details><summary>Click to expand</summary> |
| |
|
| | </details> |
| | --> |
| |
|
| | <!-- |
| | ### Out-of-Scope Use |
| |
|
| | *List how the model may foreseeably be misused and address what users ought not to do with the model.* |
| | --> |
| |
|
| | ## Evaluation |
| |
|
| | ### Metrics |
| |
|
| | #### Triplet |
| |
|
| | * Dataset: `retrieval-eval` |
| | * Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator) |
| |
|
| | | Metric | Value | |
| | |:--------------------|:-----------| |
| | | **cosine_accuracy** | **0.9835** | |
| | |
| | <!-- |
| | ## Bias, Risks and Limitations |
| | |
| | *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
| | --> |
| | |
| | <!-- |
| | ### Recommendations |
| | |
| | *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
| | --> |
| | |
| | ## Training Details |
| | |
| | ### Training Dataset |
| | |
| | #### Unnamed Dataset |
| | |
| | * Size: 42,459 training samples |
| | * Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>sentence_2</code> |
| | * Approximate statistics based on the first 1000 samples: |
| | | | sentence_0 | sentence_1 | sentence_2 | |
| | |:--------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------| |
| | | type | string | string | string | |
| | | details | <ul><li>min: 4 tokens</li><li>mean: 30.48 tokens</li><li>max: 159 tokens</li></ul> | <ul><li>min: 21 tokens</li><li>mean: 29.64 tokens</li><li>max: 125 tokens</li></ul> | <ul><li>min: 21 tokens</li><li>mean: 27.15 tokens</li><li>max: 125 tokens</li></ul> | |
| | * Samples: |
| | | sentence_0 | sentence_1 | sentence_2 | |
| | |:-----------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------| |
| | | <code>I need to ensure that only images from specific registries are used in our policy</code> | <code>Helper: lib.image.str<br>Signature: str(d)<br>Description: </code> | <code>Helper: lib.konflux.is_validating_image_index<br>Signature: is_validating_image_index<br>Description: </code> | |
| | | <code>check if check warn</code> | <code>Helper: lib.tekton.expiry_of<br>Signature: expiry_of(task)<br>Description: </code> | <code>Helper: lib.tekton.untagged_task_references<br>Signature: untagged_task_references(tasks)<br>Description: </code> | |
| | | <code>verify that task has an expiry date set.</code> | <code>Helper: lib.tekton.task_param<br>Signature: task_param(task, name)<br>Description: </code> | <code>Helper: lib.tekton.untagged_task_references<br>Signature: untagged_task_references(tasks)<br>Description: </code> | |
| | * Loss: [<code>TripletLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#tripletloss) with these parameters: |
| | ```json |
| | { |
| | "distance_metric": "TripletDistanceMetric.COSINE", |
| | "triplet_margin": 0.5 |
| | } |
| | ``` |
| | |
| | ### Training Hyperparameters |
| | #### Non-Default Hyperparameters |
| | |
| | - `eval_strategy`: steps |
| | - `per_device_train_batch_size`: 128 |
| | - `per_device_eval_batch_size`: 128 |
| | - `num_train_epochs`: 5 |
| | - `multi_dataset_batch_sampler`: round_robin |
| | |
| | #### All Hyperparameters |
| | <details><summary>Click to expand</summary> |
| | |
| | - `overwrite_output_dir`: False |
| | - `do_predict`: False |
| | - `eval_strategy`: steps |
| | - `prediction_loss_only`: True |
| | - `per_device_train_batch_size`: 128 |
| | - `per_device_eval_batch_size`: 128 |
| | - `per_gpu_train_batch_size`: None |
| | - `per_gpu_eval_batch_size`: None |
| | - `gradient_accumulation_steps`: 1 |
| | - `eval_accumulation_steps`: None |
| | - `torch_empty_cache_steps`: None |
| | - `learning_rate`: 5e-05 |
| | - `weight_decay`: 0.0 |
| | - `adam_beta1`: 0.9 |
| | - `adam_beta2`: 0.999 |
| | - `adam_epsilon`: 1e-08 |
| | - `max_grad_norm`: 1 |
| | - `num_train_epochs`: 5 |
| | - `max_steps`: -1 |
| | - `lr_scheduler_type`: linear |
| | - `lr_scheduler_kwargs`: {} |
| | - `warmup_ratio`: 0.0 |
| | - `warmup_steps`: 0 |
| | - `log_level`: passive |
| | - `log_level_replica`: warning |
| | - `log_on_each_node`: True |
| | - `logging_nan_inf_filter`: True |
| | - `save_safetensors`: True |
| | - `save_on_each_node`: False |
| | - `save_only_model`: False |
| | - `restore_callback_states_from_checkpoint`: False |
| | - `no_cuda`: False |
| | - `use_cpu`: False |
| | - `use_mps_device`: False |
| | - `seed`: 42 |
| | - `data_seed`: None |
| | - `jit_mode_eval`: False |
| | - `bf16`: False |
| | - `fp16`: False |
| | - `fp16_opt_level`: O1 |
| | - `half_precision_backend`: auto |
| | - `bf16_full_eval`: False |
| | - `fp16_full_eval`: False |
| | - `tf32`: None |
| | - `local_rank`: 0 |
| | - `ddp_backend`: None |
| | - `tpu_num_cores`: None |
| | - `tpu_metrics_debug`: False |
| | - `debug`: [] |
| | - `dataloader_drop_last`: False |
| | - `dataloader_num_workers`: 0 |
| | - `dataloader_prefetch_factor`: None |
| | - `past_index`: -1 |
| | - `disable_tqdm`: False |
| | - `remove_unused_columns`: True |
| | - `label_names`: None |
| | - `load_best_model_at_end`: False |
| | - `ignore_data_skip`: False |
| | - `fsdp`: [] |
| | - `fsdp_min_num_params`: 0 |
| | - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} |
| | - `fsdp_transformer_layer_cls_to_wrap`: None |
| | - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} |
| | - `parallelism_config`: None |
| | - `deepspeed`: None |
| | - `label_smoothing_factor`: 0.0 |
| | - `optim`: adamw_torch |
| | - `optim_args`: None |
| | - `adafactor`: False |
| | - `group_by_length`: False |
| | - `length_column_name`: length |
| | - `project`: huggingface |
| | - `trackio_space_id`: trackio |
| | - `ddp_find_unused_parameters`: None |
| | - `ddp_bucket_cap_mb`: None |
| | - `ddp_broadcast_buffers`: False |
| | - `dataloader_pin_memory`: True |
| | - `dataloader_persistent_workers`: False |
| | - `skip_memory_metrics`: True |
| | - `use_legacy_prediction_loop`: False |
| | - `push_to_hub`: False |
| | - `resume_from_checkpoint`: None |
| | - `hub_model_id`: None |
| | - `hub_strategy`: every_save |
| | - `hub_private_repo`: None |
| | - `hub_always_push`: False |
| | - `hub_revision`: None |
| | - `gradient_checkpointing`: False |
| | - `gradient_checkpointing_kwargs`: None |
| | - `include_inputs_for_metrics`: False |
| | - `include_for_metrics`: [] |
| | - `eval_do_concat_batches`: True |
| | - `fp16_backend`: auto |
| | - `push_to_hub_model_id`: None |
| | - `push_to_hub_organization`: None |
| | - `mp_parameters`: |
| | - `auto_find_batch_size`: False |
| | - `full_determinism`: False |
| | - `torchdynamo`: None |
| | - `ray_scope`: last |
| | - `ddp_timeout`: 1800 |
| | - `torch_compile`: False |
| | - `torch_compile_backend`: None |
| | - `torch_compile_mode`: None |
| | - `include_tokens_per_second`: False |
| | - `include_num_input_tokens_seen`: no |
| | - `neftune_noise_alpha`: None |
| | - `optim_target_modules`: None |
| | - `batch_eval_metrics`: False |
| | - `eval_on_start`: False |
| | - `use_liger_kernel`: False |
| | - `liger_kernel_config`: None |
| | - `eval_use_gather_object`: False |
| | - `average_tokens_across_devices`: True |
| | - `prompts`: None |
| | - `batch_sampler`: batch_sampler |
| | - `multi_dataset_batch_sampler`: round_robin |
| | - `router_mapping`: {} |
| | - `learning_rate_mapping`: {} |
| | |
| | </details> |
| | |
| | ### Training Logs |
| | | Epoch | Step | Training Loss | retrieval-eval_cosine_accuracy | |
| | |:------:|:----:|:-------------:|:------------------------------:| |
| | | 0.5 | 166 | - | 0.9731 | |
| | | 1.0 | 332 | - | 0.9786 | |
| | | 1.5 | 498 | - | 0.9794 | |
| | | 1.5060 | 500 | 0.0784 | - | |
| | | 2.0 | 664 | - | 0.9816 | |
| | | 2.5 | 830 | - | 0.9826 | |
| | | 3.0 | 996 | - | 0.9835 | |
| | |
| | |
| | ### Framework Versions |
| | - Python: 3.12.9 |
| | - Sentence Transformers: 5.2.0 |
| | - Transformers: 4.57.3 |
| | - PyTorch: 2.7.1+cu128 |
| | - Accelerate: 1.12.0 |
| | - Datasets: 4.4.1 |
| | - Tokenizers: 0.22.1 |
| | |
| | ## Citation |
| | |
| | ### BibTeX |
| | |
| | #### Sentence Transformers |
| | ```bibtex |
| | @inproceedings{reimers-2019-sentence-bert, |
| | title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", |
| | author = "Reimers, Nils and Gurevych, Iryna", |
| | booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", |
| | month = "11", |
| | year = "2019", |
| | publisher = "Association for Computational Linguistics", |
| | url = "https://arxiv.org/abs/1908.10084", |
| | } |
| | ``` |
| | |
| | #### TripletLoss |
| | ```bibtex |
| | @misc{hermans2017defense, |
| | title={In Defense of the Triplet Loss for Person Re-Identification}, |
| | author={Alexander Hermans and Lucas Beyer and Bastian Leibe}, |
| | year={2017}, |
| | eprint={1703.07737}, |
| | archivePrefix={arXiv}, |
| | primaryClass={cs.CV} |
| | } |
| | ``` |
| | |
| | <!-- |
| | ## Glossary |
| | |
| | *Clearly define terms in order to be accessible across audiences.* |
| | --> |
| | |
| | <!-- |
| | ## Model Card Authors |
| | |
| | *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.* |
| | --> |
| | |
| | <!-- |
| | ## Model Card Contact |
| | |
| | *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.* |
| | --> |
