SentenceTransformer(
(0): Transformer({'transformer_task': 'feature-extraction', 'modality_config': {'text': {'method': 'forward', 'method_output_name': 'last_hidden_state'}}, 'module_output_name': 'token_embeddings', 'architecture': 'BertModel'})
(1): Pooling({'embedding_dimension': 768, 'pooling_mode': 'mean', 'include_prompt': True})
)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 'Ɛnka sɛ, “Adɛn enti na tete nna no yɛ sene ɛnnɛ mmerɛ yi?” Onyansafoɔ mmisa nsɛm sei.',
8 'Ɛnka sɛ, “Adɛn enti na tete nna no yɛ sene ɛnnɛ mmerɛ yi?” Onyansafoɔ mmisa nsɛm sei.',
9 'Nanso, ɔsomfoɔ biara a wɔtee sika tɔɔ no no, sɛ wɔatwa no twetia deɛ a, ɔtumi we bi.',
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)
18# tensor([[1.0000, 1.0000, 0.1095],
19# [1.0000, 1.0000, 0.1095],
20# [0.1095, 0.1095, 1.0000]])sentence_0 and sentence_1| sentence_0 | sentence_1 | |
|---|---|---|
| type | string | string |
| modality | text | text |
| details |
|
|
| sentence_0 | sentence_1 |
|---|---|
Wɔde dwetɛ ne sikakɔkɔɔ hyehyɛ no; wɔde nnadewa ne asaeɛ bobɔ si hɔ sɛdeɛ ɛrenhwe fam. | Wɔde dwetɛ ne sikakɔkɔɔ hyehyɛ no; wɔde nnadewa ne asaeɛ bobɔ si hɔ sɛdeɛ ɛrenhwe fam. |
na wɔbɛtwa amumuyɛfoɔ afiri asase no so, na wɔatɔre atorofoɔ ase. | na wɔbɛtwa amumuyɛfoɔ afiri asase no so, na wɔatɔre atorofoɔ ase. |
Ɔsoro asraafoɔ a wɔhyehyɛ nwera fitaa a ani te a wɔtete apɔnkɔ fitafitaa so dii nʼakyi. | Ɔsoro asraafoɔ a wɔhyehyɛ nwera fitaa a ani te a wɔtete apɔnkɔ fitafitaa so dii nʼakyi. |
MultipleNegativesRankingLoss with these parameters:
1{
2 "scale": 20.0,
3 "similarity_fct": "cos_sim",
4 "gather_across_devices": false,
5 "directions": [
6 "query_to_doc"
7 ],
8 "partition_mode": "joint",
9 "hardness_mode": null,
10 "hardness_strength": 0.0
11}per_device_train_batch_size: 16num_train_epochs: 1per_device_eval_batch_size: 16multi_dataset_batch_sampler: round_robinper_device_train_batch_size: 16num_train_epochs: 1max_steps: -1learning_rate: 5e-05lr_scheduler_type: linearlr_scheduler_kwargs: Nonewarmup_steps: 0optim: adamw_torch_fusedoptim_args: Noneweight_decay: 0.0adam_beta1: 0.9adam_beta2: 0.999adam_epsilon: 1e-08optim_target_modules: Nonegradient_accumulation_steps: 1average_tokens_across_devices: Truemax_grad_norm: 1label_smoothing_factor: 0.0bf16: Falsefp16: Falsebf16_full_eval: Falsefp16_full_eval: Falsetf32: Nonegradient_checkpointing: Falsegradient_checkpointing_kwargs: Nonetorch_compile: Falsetorch_compile_backend: Nonetorch_compile_mode: Noneuse_liger_kernel: Falseliger_kernel_config: Noneuse_cache: Falseneftune_noise_alpha: Nonetorch_empty_cache_steps: Noneauto_find_batch_size: Falselog_on_each_node: Truelogging_nan_inf_filter: Trueinclude_num_input_tokens_seen: nolog_level: passivelog_level_replica: warningdisable_tqdm: Falseproject: huggingfacetrackio_space_id: Nonetrackio_bucket_id: Nonetrackio_static_space_id: Noneper_device_eval_batch_size: 16prediction_loss_only: Trueeval_on_start: Falseeval_do_concat_batches: Trueeval_use_gather_object: Falseeval_accumulation_steps: Noneinclude_for_metrics: []batch_eval_metrics: Falsesave_only_model: Falsesave_on_each_node: Falseenable_jit_checkpoint: Falsepush_to_hub: Falsehub_private_repo: Nonehub_model_id: Nonehub_strategy: every_savehub_always_push: Falsehub_revision: Noneload_best_model_at_end: Falseignore_data_skip: Falserestore_callback_states_from_checkpoint: Falsefull_determinism: Falseseed: 42data_seed: Noneuse_cpu: Falseaccelerator_config: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}parallelism_config: Nonedataloader_drop_last: Falsedataloader_num_workers: 0dataloader_pin_memory: Truedataloader_persistent_workers: Falsedataloader_prefetch_factor: Noneremove_unused_columns: Truelabel_names: Nonetrain_sampling_strategy: randomlength_column_name: lengthddp_find_unused_parameters: Noneddp_bucket_cap_mb: Noneddp_broadcast_buffers: Falseddp_static_graph: Noneddp_backend: Noneddp_timeout: 1800fsdp: Nonefsdp_config: Nonedeepspeed: Nonedebug: []skip_memory_metrics: Truedo_predict: Falseresume_from_checkpoint: Nonewarmup_ratio: Nonelocal_rank: -1prompts: Nonebatch_sampler: batch_samplermulti_dataset_batch_sampler: round_robinrouter_mapping: {}learning_rate_mapping: {}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{oord2019representationlearningcontrastivepredictive,
2 title={Representation Learning with Contrastive Predictive Coding},
3 author={Aaron van den Oord and Yazhe Li and Oriol Vinyals},
4 year={2019},
5 eprint={1807.03748},
6 archivePrefix={arXiv},
7 primaryClass={cs.LG},
8 url={https://arxiv.org/abs/1807.03748},
9}