SentenceTransformer based on sentence-transformers/multi-qa-MiniLM-L6-cos-v1
This is a sentence-transformers model finetuned from sentence-transformers/multi-qa-MiniLM-L6-cos-v1. 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/multi-qa-MiniLM-L6-cos-v1
- Maximum Sequence Length: 512 tokens
- Output Dimensionality: 384 tokens
- Similarity Function: Cosine Similarity
Model Sources
- Documentation: Sentence Transformers Documentation
- Repository: Sentence Transformers on GitHub
- Hugging Face: Sentence Transformers on Hugging Face
Full Model Architecture
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: 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:
pip install -U sentence-transformers
Then you can load this model and run inference.
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("Trelis/multi-qa-MiniLM-L6-cos-v1-ft-pairs")
# Run inference
sentences = [
'What is the ruling if an attacking player intentionally passes the ball to a defending player seeking a rebound or restart of the touch count?',
'ball goes to ground, a change of possession occurs. 12. 6 if a player from the defending team unintentionally makes contact with the ball in flight and the ball is retrieved by an attacking player, play and the touch count continues. 12. 7 a player from the attacking team cannot pass the ball into a defending player intentionally seeking a rebound or a restart of the touch count. ruling = a penalty to the defending team at the point of the pass. 13 the rollball 13. 1 the attacking player is to position on the mark, face the opponent ’ s try line, make a genuine attempt to stand parallel to the sidelines, place the ball on the ground between the feet in a controlled manner and : 13. 1. 1 step forward over the ball ; or 13. 1. 2 roll the ball back between the feet no more than one ( 1 ) metre ; or 13. 1. 3 pass a foot over the ball. ruling = a change of possession to the defending team at the point of the infringement. 13. 2 a player must perform the rollball on the mark. ruling = a penalty to the defending team at the point of the infringement. 13. 3 a player must not perform a voluntary rollball',
'. see appendix 1. kick strike or propel forcibly with the foot, a blow or forceful thrust with the foot to the ball. a tap to commence or recommence play or a penalty tap is not defined as a kick. line markings markings of the field of play. see appendix 1. link the player beside the wing player. mark ( for a tap ) the centre of the halfway line for the commencement or recommencement of play, or the position where a penalty tap is awarded as a result of an infringement. mark ( for a touch ) the position in the field of play where the player in possession was at the time the touch was made. fit playing rules - 5th edition 2 copyright © touch football australia 2020 middle the player inside the link player. nta national touch association as defined in the fit constitution. obstruction a deliberate attempt by either an attacking or defending player to gain an unfair advantage by interfering with the opposition to prevent them from gaining a rightful advantage. offside ( attacker ) an attacking player in a position forward of the ball. offside ( defender ) a defending player in a position closer than seven ( 7 ) metres from the mark of the rollball ; or ten ( 10 ) metres from the mark',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
Training Details
Training Hyperparameters
Non-Default Hyperparameters
eval_strategy
: stepsper_device_train_batch_size
: 16per_device_eval_batch_size
: 16learning_rate
: 0.0001num_train_epochs
: 2lr_scheduler_type
: cosinewarmup_ratio
: 0.3
All Hyperparameters
Click to expand
overwrite_output_dir
: Falsedo_predict
: Falseeval_strategy
: stepsprediction_loss_only
: Trueper_device_train_batch_size
: 16per_device_eval_batch_size
: 16per_gpu_train_batch_size
: Noneper_gpu_eval_batch_size
: Nonegradient_accumulation_steps
: 1eval_accumulation_steps
: Nonelearning_rate
: 0.0001weight_decay
: 0.0adam_beta1
: 0.9adam_beta2
: 0.999adam_epsilon
: 1e-08max_grad_norm
: 1.0num_train_epochs
: 2max_steps
: -1lr_scheduler_type
: cosinelr_scheduler_kwargs
: {}warmup_ratio
: 0.3warmup_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
: Falsebatch_sampler
: batch_samplermulti_dataset_batch_sampler
: proportional
Training Logs
Epoch | Step | Training Loss | loss |
---|---|---|---|
0.1429 | 2 | 1.0897 | - |
0.2857 | 4 | 1.3506 | 1.1206 |
0.4286 | 6 | 1.1968 | - |
0.5714 | 8 | 1.4074 | 1.0205 |
0.7143 | 10 | 1.3476 | - |
0.8571 | 12 | 1.0062 | 1.0278 |
1.0 | 14 | 1.4792 | - |
1.1429 | 16 | 0.8863 | 1.1568 |
1.2857 | 18 | 0.5465 | - |
1.4286 | 20 | 0.5672 | 1.1830 |
1.5714 | 22 | 0.5482 | - |
1.7143 | 24 | 0.7633 | 1.1838 |
1.8571 | 26 | 0.5931 | - |
2.0 | 28 | 0.4969 | 1.1800 |
Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.0.1
- Transformers: 4.42.3
- PyTorch: 2.1.1+cu121
- Accelerate: 0.31.0
- Datasets: 2.17.1
- Tokenizers: 0.19.1
Citation
BibTeX
Sentence Transformers
@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",
}
MultipleNegativesRankingLoss
@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}
}
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