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SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 768, '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})
(2): Normalize()
)pip install -U sentence-transformers1from sentence_transformers import SentenceTransformer
2
3# Download from the 🤗 Hub
4model = SentenceTransformer("sentence_transformers_model_id")
5# Run inference
6sentences = [
7 'What types of risks should be identified and mitigated before the deployment of an automated system?',
8 'Risk identification and mitigation. Before deployment, and in a proactive and ongoing manner, poten -\ntial risks of the automated system should be identified and mitigated. Identified risks should focus on the potential for meaningful impact on people’s rights, opportunities, or access and include those to impacted communities that may not be direct users of the automated system, risks resulting from purposeful misuse of the system, and other concerns identified via the consultation process. Assessment and, where possible, mea\n-',
9 'APPENDIX\nSystems that impact the safety of communities such as automated traffic control systems, elec \n-ctrical grid controls, smart city technologies, and industrial emissions and environmental\nimpact control algorithms; and\nSystems related to access to benefits or services or assignment of penalties such as systems that',
10]
11embeddings = model.encode(sentences)
12print(embeddings.shape)
13# [3, 768]
14
15# Get the similarity scores for the embeddings
16similarities = model.similarity(embeddings, embeddings)
17print(similarities.shape)
18# [3, 3]InformationRetrievalEvaluator| Metric | Value |
|---|---|
| cosine_accuracy@1 | 0.815 |
| cosine_accuracy@3 | 0.93 |
| cosine_accuracy@5 | 0.945 |
| cosine_accuracy@10 | 0.98 |
| cosine_precision@1 | 0.815 |
| cosine_precision@3 | 0.31 |
| cosine_precision@5 | 0.189 |
| cosine_precision@10 | 0.098 |
| cosine_recall@1 | 0.815 |
| cosine_recall@3 | 0.93 |
| cosine_recall@5 | 0.945 |
| cosine_recall@10 | 0.98 |
| cosine_ndcg@10 | 0.9031 |
| cosine_mrr@10 | 0.8781 |
| cosine_map@100 | 0.8795 |
| dot_accuracy@1 | 0.815 |
| dot_accuracy@3 | 0.93 |
| dot_accuracy@5 | 0.945 |
| dot_accuracy@10 | 0.98 |
| dot_precision@1 | 0.815 |
| dot_precision@3 | 0.31 |
| dot_precision@5 | 0.189 |
| dot_precision@10 | 0.098 |
| dot_recall@1 | 0.815 |
| dot_recall@3 | 0.93 |
| dot_recall@5 | 0.945 |
| dot_recall@10 | 0.98 |
| dot_ndcg@10 | 0.9031 |
| dot_mrr@10 | 0.8781 |
| dot_map@100 | 0.8795 |
sentence_0 and sentence_1| sentence_0 | sentence_1 | |
|---|---|---|
| type | string | string |
| details |
|
|
| sentence_0 | sentence_1 |
|---|---|
What is the purpose of the AI Bill of Rights mentioned in the context? | BLUEPRINT FOR AN [object Object]AI B ILL OF [object Object]RIGHTS [object Object]MAKING AUTOMATED [object Object]SYSTEMS WORK FOR [object Object]THE AMERICAN PEOPLE [object Object]OCTOBER 2022 |
When was the Blueprint for an AI Bill of Rights published? | BLUEPRINT FOR AN [object Object]AI B ILL OF [object Object]RIGHTS [object Object]MAKING AUTOMATED [object Object]SYSTEMS WORK FOR [object Object]THE AMERICAN PEOPLE [object Object]OCTOBER 2022 |
What is 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 [object Object]The Blueprint for an AI Bill of Rights: Making Automated Systems Work for the American People was [object Object]published by the White House Office of Science and Technology Policy in October 2022. This framework was [object Object]released one year after OSTP announced the launch of a process to develop “a bill of rights for an AI-powered [object Object]world.” Its release follows a year of public engagement to inform this initiative. The framework is available [object Object]online at: [object Object] [object Object]About the Office of Science and Technology Policy [object Object]The Office of Science and Technology Policy (OSTP) was established by the National Science and Technology |
MatryoshkaLoss with these parameters:
1{
2 "loss": "MultipleNegativesRankingLoss",
3 "matryoshka_dims": [
4 768,
5 512,
6 256,
7 128,
8 64
9 ],
10 "matryoshka_weights": [
11 1,
12 1,
13 1,
14 1,
15 1
16 ],
17 "n_dims_per_step": -1
18}eval_strategy: stepsper_device_train_batch_size: 20per_device_eval_batch_size: 20num_train_epochs: 5multi_dataset_batch_sampler: round_robinoverwrite_output_dir: Falsedo_predict: Falseeval_strategy: stepsprediction_loss_only: Trueper_device_train_batch_size: 20per_device_eval_batch_size: 20per_gpu_train_batch_size: Noneper_gpu_eval_batch_size: Nonegradient_accumulation_steps: 1eval_accumulation_steps: Nonetorch_empty_cache_steps: Nonelearning_rate: 5e-05weight_decay: 0.0adam_beta1: 0.9adam_beta2: 0.999adam_epsilon: 1e-08max_grad_norm: 1num_train_epochs: 5max_steps: -1lr_scheduler_type: linearlr_scheduler_kwargs: {}warmup_ratio: 0.0warmup_steps: 0log_level: passivelog_level_replica: warninglog_on_each_node: Truelogging_nan_inf_filter: Truesave_safetensors: Truesave_on_each_node: Falsesave_only_model: Falserestore_callback_states_from_checkpoint: Falseno_cuda: Falseuse_cpu: Falseuse_mps_device: Falseseed: 42data_seed: Nonejit_mode_eval: Falseuse_ipex: Falsebf16: Falsefp16: Falsefp16_opt_level: O1half_precision_backend: autobf16_full_eval: Falsefp16_full_eval: Falsetf32: Nonelocal_rank: 0ddp_backend: Nonetpu_num_cores: Nonetpu_metrics_debug: Falsedebug: []dataloader_drop_last: Falsedataloader_num_workers: 0dataloader_prefetch_factor: Nonepast_index: -1disable_tqdm: Falseremove_unused_columns: Truelabel_names: Noneload_best_model_at_end: Falseignore_data_skip: Falsefsdp: []fsdp_min_num_params: 0fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}fsdp_transformer_layer_cls_to_wrap: Noneaccelerator_config: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}deepspeed: Nonelabel_smoothing_factor: 0.0optim: adamw_torchoptim_args: Noneadafactor: Falsegroup_by_length: Falselength_column_name: lengthddp_find_unused_parameters: Noneddp_bucket_cap_mb: Noneddp_broadcast_buffers: Falsedataloader_pin_memory: Truedataloader_persistent_workers: Falseskip_memory_metrics: Trueuse_legacy_prediction_loop: Falsepush_to_hub: Falseresume_from_checkpoint: Nonehub_model_id: Nonehub_strategy: every_savehub_private_repo: Falsehub_always_push: Falsegradient_checkpointing: Falsegradient_checkpointing_kwargs: Noneinclude_inputs_for_metrics: Falseeval_do_concat_batches: Truefp16_backend: autopush_to_hub_model_id: Nonepush_to_hub_organization: Nonemp_parameters:auto_find_batch_size: Falsefull_determinism: Falsetorchdynamo: Noneray_scope: lastddp_timeout: 1800torch_compile: Falsetorch_compile_backend: Nonetorch_compile_mode: Nonedispatch_batches: Nonesplit_batches: Noneinclude_tokens_per_second: Falseinclude_num_input_tokens_seen: Falseneftune_noise_alpha: Noneoptim_target_modules: Nonebatch_eval_metrics: Falseeval_on_start: Falseeval_use_gather_object: Falsebatch_sampler: batch_samplermulti_dataset_batch_sampler: round_robin| Epoch | Step | cosine_map@100 |
|---|---|---|
| 1.0 | 40 | 0.8784 |
| 1.25 | 50 | 0.8759 |
| 2.0 | 80 | 0.8795 |
1@inproceedings{reimers-2019-sentence-bert,
2 title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
3 author = "Reimers, Nils and Gurevych, Iryna",
4 booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
5 month = "11",
6 year = "2019",
7 publisher = "Association for Computational Linguistics",
8 url = "https://arxiv.org/abs/1908.10084",
9}1@misc{kusupati2024matryoshka,
2 title={Matryoshka Representation Learning},
3 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},
4 year={2024},
5 eprint={2205.13147},
6 archivePrefix={arXiv},
7 primaryClass={cs.LG}
8}1@misc{henderson2017efficient,
2 title={Efficient Natural Language Response Suggestion for Smart Reply},
3 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},
4 year={2017},
5 eprint={1705.00652},
6 archivePrefix={arXiv},
7 primaryClass={cs.CL}
8}