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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()
)pip install -U sentence-transformers1from sentence_transformers import SentenceTransformer
2
3# Download from the 🤗 Hub
4model = SentenceTransformer("zacCMU/miniLM2-ENG")
5# Run inference
6sentences = [
7 '10.7 Information to be provided to the FIA and Competitors \na) In order that an FIA observer may be appointed, Competitors must inform the FIA and all \nother Competitors of any planned TPC, PE or DE at least 72 hours before it is due to \ncommence, and the following information must be provided: \ni) The precise specification of the car(s) to be used. ii) The name(s) of the driver(s). iii) The type of activity.',
8 'Competitors must notify the FIA and other teams at least 72 hours in advance of any planned technical testing, physical evaluations, or development exercises, providing detailed information about the cars, drivers, and nature of the activity.',
9 "The aerodynamic design of a Formula 1 car's rear wing is crucial in determining its overall downforce and drag characteristics, requiring a delicate balance between speed and stability.",
10]
11embeddings = model.encode(sentences)
12print(embeddings.shape)
13# [3, 384]
14
15# Get the similarity scores for the embeddings
16similarities = model.similarity(embeddings, embeddings)
17print(similarities)
18# tensor([[ 1.0000, -0.6238, 0.9745],
19# [-0.6238, 1.0000, -0.6029],
20# [ 0.9745, -0.6029, 1.0000]])anchor, positive, and negative| anchor | positive | negative | |
|---|---|---|---|
| type | string | string | string |
| details |
|
|
|
| anchor | positive | negative |
|---|---|---|
A penalty in accordance with Article 54.3d) will be imposed on any [object Object]driver who fails to start the race from the pit lane. If any driver needs assistance after the fifteen (15) second signal, he must raise his arm and, [object Object]when the remainder of the cars able to do so have left the pit lane, marshals will be instructed [object Object]to push the car into the inner lane. In this case, marshals with yellow flags will stand beside any [object Object]car concerned to warn drivers behind. | A driver who fails to start the race from the pit lane will incur a penalty. If a driver requires assistance after the 15-second signal, they must signal for help and marshals will then guide their car into the inner lane, warning other drivers with yellow flags. | The aerodynamic design of modern Formula 1 cars requires a delicate balance between downforce and drag to achieve optimal speed on the track. |
If a driver wishes to leave his car before it is weighed , he [object Object]must ask the Technical Delegate to weigh him in order that this weight may be added to [object Object]that of the car. e) If a car stops on the circuit during the qualifying session or the sprint qualifying session [object Object]and the driver leaves the car, he must go to the FIA garage immediately on his return to [object Object]the pit lane in order for his weight to be established. 35.2 After the sprint session or the race any classified car may be weighed. | To avoid penalties, a driver must ensure their weight is accurately recorded before leaving their car, either by having the Technical Delegate weigh them or by being weighed in the FIA garage after returning to the pit lane. This process is crucial during qualifying sessions, sprint qualifying sessions, and after the sprint session or the race. | The aerodynamic design of a Formula 1 car's rear wing plays a crucial role in generating downforce, but its impact on the overall handling and stability of the vehicle is often overlooked by teams in their pursuit of speed. |
d) When leaving the pits a driver may overtake, or be overtaken by, another car on the track [object Object]before he reaches the second safety car line. e) When the safety car is returning to the pits it may be overtaken by cars on the track once [object Object]it has reached the first safety car line. f) Whilst in the pit entry road, pit lane or pit exit road a driver may overtake another car [object Object]which is also in one of these three areas. | When exiting the pits, a driver is allowed to overtake or be overtaken by another car on the track before reaching the second safety car line. Additionally, the safety car can be overtaken by cars on the track once it has reached the first safety car line, and drivers can also overtake each other while in the pit entry road, pit lane, or pit exit road. | The aerodynamic design of modern Formula 1 cars relies heavily on complex computational fluid dynamics simulations to optimize their downforce and drag characteristics. |
TripletLoss with these parameters:
1{
2 "distance_metric": "TripletDistanceMetric.EUCLIDEAN",
3 "triplet_margin": 5
4}per_device_train_batch_size: 16learning_rate: 1e-05num_train_epochs: 4overwrite_output_dir: Falsedo_predict: Falseeval_strategy: noprediction_loss_only: Trueper_device_train_batch_size: 16per_device_eval_batch_size: 8per_gpu_train_batch_size: Noneper_gpu_eval_batch_size: Nonegradient_accumulation_steps: 1eval_accumulation_steps: Nonetorch_empty_cache_steps: Nonelearning_rate: 1e-05weight_decay: 0.0adam_beta1: 0.9adam_beta2: 0.999adam_epsilon: 1e-08max_grad_norm: 1.0num_train_epochs: 4max_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: 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}parallelism_config: Nonedeepspeed: Nonelabel_smoothing_factor: 0.0optim: adamw_torch_fusedoptim_args: Noneadafactor: Falsegroup_by_length: Falselength_column_name: lengthproject: huggingfacetrackio_space_id: trackioddp_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: Nonehub_always_push: Falsehub_revision: Nonegradient_checkpointing: Falsegradient_checkpointing_kwargs: Noneinclude_inputs_for_metrics: Falseinclude_for_metrics: []eval_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: Noneinclude_tokens_per_second: Falseinclude_num_input_tokens_seen: noneftune_noise_alpha: Noneoptim_target_modules: Nonebatch_eval_metrics: Falseeval_on_start: Falseuse_liger_kernel: Falseliger_kernel_config: Noneeval_use_gather_object: Falseaverage_tokens_across_devices: Trueprompts: Nonebatch_sampler: batch_samplermulti_dataset_batch_sampler: proportionalrouter_mapping: {}learning_rate_mapping: {}| Epoch | Step | Training Loss |
|---|---|---|
| 0.25 | 10 | 5.5019 |
| 0.5 | 20 | 5.2724 |
| 0.75 | 30 | 5.1275 |
| 1.0 | 40 | 4.999 |
| 1.25 | 50 | 4.8488 |
| 1.5 | 60 | 4.7919 |
| 1.75 | 70 | 4.6734 |
| 2.0 | 80 | 4.4696 |
| 2.25 | 90 | 4.4078 |
| 2.5 | 100 | 4.2232 |
| 2.75 | 110 | 4.1736 |
| 3.0 | 120 | 4.0837 |
| 3.25 | 130 | 4.0113 |
| 3.5 | 140 | 4.0376 |
| 3.75 | 150 | 3.9134 |
| 4.0 | 160 | 3.9853 |
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{hermans2017defense,
2 title={In Defense of the Triplet Loss for Person Re-Identification},
3 author={Alexander Hermans and Lucas Beyer and Bastian Leibe},
4 year={2017},
5 eprint={1703.07737},
6 archivePrefix={arXiv},
7 primaryClass={cs.CV}
8}