--- base_model: Alibaba-NLP/gte-large-en-v1.5 library_name: sentence-transformers metrics: - cosine_accuracy@1 - cosine_accuracy@3 - cosine_accuracy@5 - cosine_accuracy@10 - cosine_precision@1 - cosine_precision@3 - cosine_precision@5 - cosine_precision@10 - cosine_recall@1 - cosine_recall@3 - cosine_recall@5 - cosine_recall@10 - cosine_ndcg@10 - cosine_mrr@10 - cosine_map@100 - dot_accuracy@1 - dot_accuracy@3 - dot_accuracy@5 - dot_accuracy@10 - dot_precision@1 - dot_precision@3 - dot_precision@5 - dot_precision@10 - dot_recall@1 - dot_recall@3 - dot_recall@5 - dot_recall@10 - dot_ndcg@10 - dot_mrr@10 - dot_map@100 pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:500 - loss:MatryoshkaLoss - loss:MultipleNegativesRankingLoss widget: - source_sentence: "narrow identified goals, to avoid \"mission creep.\" Anticipated\ \ data collection should be determined to be \nstrictly necessary to the identified\ \ goals and should be minimized as much as possible. Data collected based on \n\ these identified goals and for a specific context should not be used in a different\ \ context without assessing for \nnew privacy risks and implementing appropriate\ \ mitigation measures, which may include express consent." sentences: - What measures should be taken if data collected for specific identified goals is to be used in a different context? - What measures should be taken to ensure the privacy of sensitive data and limit access to it? - What special requirements are mentioned in the white paper regarding national security and defense activities in relation to trustworthy artificial intelligence? - source_sentence: '• Karen Levy, Assistant Professor, Department of Information Science, Cornell University • Natasha Duarte, Project Director, Upturn • Elana Zeide, Assistant Professor, University of Nebraska College of Law • Fabian Rogers, Constituent Advocate, Office of NY State Senator Jabari Brisport and Community Advocate and Floor Captain, Atlantic Plaza Towers Tenants Association The individual panelists described the ways in which AI systems and other technologies are increasingly being' sentences: - What are some of the challenges posed to democracy by the use of technology and automated systems, as mentioned in the foreword? - What principles has the U.S. Intelligence Community developed to guide personnel in the ethical use of AI? - What roles do the panelists hold in relation to the discussion on AI systems and technology? - source_sentence: "impacts disfavoring people based on their race, color, ethnicity,\ \ \nsex \n(including \npregnancy, \nchildbirth, \nand \nrelated \nmedical \nconditions,\ \ \ngender \nidentity, \nintersex \nstatus, \nand \nsexual \norientation), religion,\ \ age, national origin, disability, veteran status," sentences: - What does the term "HUMAN ALTERNATIVES" refer to in the context provided? - What types of discrimination are mentioned in the context? - What are the expectations for automated systems in relation to public protection from surveillance? - source_sentence: "establish and maintain the capabilities that will allow individuals\ \ to use their own automated systems to help \nthem make consent, access, and\ \ control decisions in a complex data ecosystem. Capabilities include machine\ \ \nreadable data, standardized data formats, metadata or tags for expressing\ \ data processing permissions and \npreferences and data provenance and lineage,\ \ context of use and access-specific tags, and training models for \nassessing\ \ privacy risk." sentences: - What measures should be taken to ensure that independent evaluations of algorithmic discrimination are conducted while balancing individual privacy and data access needs? - What capabilities are necessary for individuals to effectively manage consent and control decisions in a complex data ecosystem? - What are some examples of classifications that are protected by law against discrimination? - source_sentence: "SAFE AND EFFECTIVE \nSYSTEMS \nWHAT SHOULD BE EXPECTED OF AUTOMATED\ \ SYSTEMS\nThe expectations for automated systems are meant to serve as a blueprint\ \ for the development of additional \ntechnical standards and practices that are\ \ tailored for particular sectors and contexts. \nDerived data sources tracked\ \ and reviewed carefully. Data that is derived from other data through" sentences: - What is the purpose of the expectations set for automated systems in relation to technical standards and practices? - What factors influence the appropriate application of the principles outlined in the white paper regarding automated systems? - What actions can a court take if a federal agency fails to comply with the Privacy Act regarding an individual's records? model-index: - name: SentenceTransformer based on Alibaba-NLP/gte-large-en-v1.5 results: - task: type: information-retrieval name: Information Retrieval dataset: name: Unknown type: unknown metrics: - type: cosine_accuracy@1 value: 0.8933333333333333 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.9866666666666667 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.9866666666666667 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 1.0 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.8933333333333333 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.3288888888888888 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.1973333333333333 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09999999999999998 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.8933333333333333 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.9866666666666667 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.9866666666666667 name: Cosine Recall@5 - type: cosine_recall@10 value: 1.0 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.954918824730161 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.9396825396825398 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.9396825396825398 name: Cosine Map@100 - type: dot_accuracy@1 value: 0.8933333333333333 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.9866666666666667 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.9866666666666667 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 1.0 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.8933333333333333 name: Dot Precision@1 - type: dot_precision@3 value: 0.3288888888888888 name: Dot Precision@3 - type: dot_precision@5 value: 0.1973333333333333 name: Dot Precision@5 - type: dot_precision@10 value: 0.09999999999999998 name: Dot Precision@10 - type: dot_recall@1 value: 0.8933333333333333 name: Dot Recall@1 - type: dot_recall@3 value: 0.9866666666666667 name: Dot Recall@3 - type: dot_recall@5 value: 0.9866666666666667 name: Dot Recall@5 - type: dot_recall@10 value: 1.0 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.954918824730161 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.9396825396825398 name: Dot Mrr@10 - type: dot_map@100 value: 0.9396825396825398 name: Dot Map@100 --- # SentenceTransformer based on Alibaba-NLP/gte-large-en-v1.5 This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Alibaba-NLP/gte-large-en-v1.5](https://huggingface.co/Alibaba-NLP/gte-large-en-v1.5) on the json dataset. It maps sentences & paragraphs to a 1024-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:** [Alibaba-NLP/gte-large-en-v1.5](https://huggingface.co/Alibaba-NLP/gte-large-en-v1.5) - **Maximum Sequence Length:** 8192 tokens - **Output Dimensionality:** 1024 tokens - **Similarity Function:** Cosine Similarity - **Training Dataset:** - json ### Model Sources - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/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': 8192, 'do_lower_case': False}) with Transformer model: NewModel (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) ) ``` ## 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 = [ 'SAFE AND EFFECTIVE \nSYSTEMS \nWHAT SHOULD BE EXPECTED OF AUTOMATED SYSTEMS\nThe expectations for automated systems are meant to serve as a blueprint for the development of additional \ntechnical standards and practices that are tailored for particular sectors and contexts. \nDerived data sources tracked and reviewed carefully. Data that is derived from other data through', 'What is the purpose of the expectations set for automated systems in relation to technical standards and practices?', 'What factors influence the appropriate application of the principles outlined in the white paper regarding automated systems?', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 1024] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` ## Evaluation ### Metrics #### Information Retrieval * Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | Metric | Value | |:--------------------|:-----------| | cosine_accuracy@1 | 0.8933 | | cosine_accuracy@3 | 0.9867 | | cosine_accuracy@5 | 0.9867 | | cosine_accuracy@10 | 1.0 | | cosine_precision@1 | 0.8933 | | cosine_precision@3 | 0.3289 | | cosine_precision@5 | 0.1973 | | cosine_precision@10 | 0.1 | | cosine_recall@1 | 0.8933 | | cosine_recall@3 | 0.9867 | | cosine_recall@5 | 0.9867 | | cosine_recall@10 | 1.0 | | cosine_ndcg@10 | 0.9549 | | cosine_mrr@10 | 0.9397 | | **cosine_map@100** | **0.9397** | | dot_accuracy@1 | 0.8933 | | dot_accuracy@3 | 0.9867 | | dot_accuracy@5 | 0.9867 | | dot_accuracy@10 | 1.0 | | dot_precision@1 | 0.8933 | | dot_precision@3 | 0.3289 | | dot_precision@5 | 0.1973 | | dot_precision@10 | 0.1 | | dot_recall@1 | 0.8933 | | dot_recall@3 | 0.9867 | | dot_recall@5 | 0.9867 | | dot_recall@10 | 1.0 | | dot_ndcg@10 | 0.9549 | | dot_mrr@10 | 0.9397 | | dot_map@100 | 0.9397 | ## Training Details ### Training Dataset #### json * Dataset: json * Size: 500 training samples * Columns: anchor and positive * Approximate statistics based on the first 500 samples: | | anchor | positive | |:--------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | anchor | positive | |:--------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | What is the primary purpose of the AI Bill of Rights outlined in the October 2022 blueprint? | BLUEPRINT FOR AN
AI BILL OF
RIGHTS
MAKING AUTOMATED
SYSTEMS WORK FOR
THE AMERICAN PEOPLE
OCTOBER 2022 | | What was the purpose of the Blueprint for an AI Bill of Rights published by the White House Office of Science and Technology Policy? | About this Document
The Blueprint for an AI Bill of Rights: Making Automated Systems Work for the American People was
published by the White House Office of Science and Technology Policy in October 2022. This framework was
released one year after OSTP announced the launch of a process to develop “a bill of rights for an AI-powered | | What initiative did the OSTP announce a year prior to the release of the framework for a bill of rights for an AI-powered world? | released one year after OSTP announced the launch of a process to develop “a bill of rights for an AI-powered
world.” Its release follows a year of public engagement to inform this initiative. The framework is available
online at: https://www.whitehouse.gov/ostp/ai-bill-of-rights
About the Office of Science and Technology Policy
The Office of Science and Technology Policy (OSTP) was established by the National Science and Technology | * Loss: [MatryoshkaLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 1024, 512, 256, 128, 64 ], "matryoshka_weights": [ 1, 1, 1, 1, 1 ], "n_dims_per_step": -1 } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: epoch - `per_device_train_batch_size`: 32 - `per_device_eval_batch_size`: 16 - `gradient_accumulation_steps`: 16 - `learning_rate`: 2e-05 - `num_train_epochs`: 5 - `lr_scheduler_type`: cosine - `warmup_ratio`: 0.1 - `bf16`: True - `tf32`: True - `load_best_model_at_end`: True - `optim`: adamw_torch_fused - `batch_sampler`: no_duplicates #### All Hyperparameters
Click to expand - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: epoch - `prediction_loss_only`: True - `per_device_train_batch_size`: 32 - `per_device_eval_batch_size`: 16 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 16 - `eval_accumulation_steps`: None - `torch_empty_cache_steps`: None - `learning_rate`: 2e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 5 - `max_steps`: -1 - `lr_scheduler_type`: cosine - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.1 - `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 - `use_ipex`: False - `bf16`: True - `fp16`: False - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: True - `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`: True - `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} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch_fused - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `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`: False - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `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 - `dispatch_batches`: None - `split_batches`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `eval_on_start`: False - `eval_use_gather_object`: False - `batch_sampler`: no_duplicates - `multi_dataset_batch_sampler`: proportional
### Training Logs | Epoch | Step | cosine_map@100 | |:-----:|:----:|:--------------:| | 1.0 | 1 | 0.9022 | | 2.0 | 2 | 0.9311 | | 3.0 | 3 | 0.9397 | ### Framework Versions - Python: 3.10.12 - Sentence Transformers: 3.1.1 - Transformers: 4.44.2 - PyTorch: 2.4.1+cu121 - Accelerate: 0.34.2 - Datasets: 3.0.1 - Tokenizers: 0.19.1 ## 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", } ``` #### MatryoshkaLoss ```bibtex @misc{kusupati2024matryoshka, title={Matryoshka Representation Learning}, author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi}, year={2024}, eprint={2205.13147}, archivePrefix={arXiv}, primaryClass={cs.LG} } ``` #### MultipleNegativesRankingLoss ```bibtex @misc{henderson2017efficient, title={Efficient Natural Language Response Suggestion for Smart Reply}, author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, year={2017}, eprint={1705.00652}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```