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TARARARAK/HGU_rulebook-fine-tuned-Kure-v1-article_MultipleNegativesRankingLoss_fold1_7_5e-06

sourceHugging Faceupdated 1y agoView on Hugging Face

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Model Card

SentenceTransformer based on nlpai-lab/KURE-v1

This is a sentence-transformers model finetuned from nlpai-lab/KURE-v1. 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: nlpai-lab/KURE-v1
—Maximum Sequence Length: 512 tokens
—Output Dimensionality: 1024 dimensions
—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: XLMRobertaModel 
  (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})
  (2): Normalize()
)

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 = [
    '한동대학교 교수회는 어떻게 구성되나요?',
    '제 69 조 (구성)\n교수회는 조교수 이상의 전임교원으로 구성한다.',
    '제 84 조 (도서관)\n이 대학교에 도서관을 두며 운영에 관한 세부사항은 따로 정한다.',
]
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]

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Downstream Usage (Sentence Transformers)

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Training Details

Training Dataset

Unnamed Dataset

—Size: 69 training samples
—Columns: <code>sentence0</code> and <code>sentence1</code>
—Approximate statistics based on the first 69 samples: | | sentence0 | sentence1 | |:--------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------| | type | string | string | | details | <ul><li>min: 13 tokens</li><li>mean: 24.23 tokens</li><li>max: 43 tokens</li></ul> | <ul><li>min: 18 tokens</li><li>mean: 91.19 tokens</li><li>max: 435 tokens</li></ul> |
—Samples: | sentence0 | sentence1 | |:-----------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>한동대에서는 학생들이 어떤 방식으로 성장하도록 장려하나요?</code> | <code>제 51 조 (학생활동) 학생은 이 대학교의 건학정신에 따라 덕성을 기르고 교칙을 준수하며 전심ᆞ성의로 학업에 종사하고 신체를 단련하여 사회의 지도자가 될 자질을 닦아야 한다.</code> | | <code>한동대학교 교무회의에서는 어떤 사항들을 심의하나요</code> | <code>제 76 조 (심의사항) 교무회의는 다음 사항을 심의한다. 학칙 및 제규정의 제정 및 개폐에 관한 사항. 교수회의 안건중 중요한 사항. 기타 총장이 필요하다고 인정하는 사항.</code> | | <code>한동대학교의 교훈, 교육이념, 교육목적과 목표는 무엇인가요?</code> | <code>제 2 조 (교훈, 교육이념, 교육목적, 교육목표) 이 대학교의 교훈, 교육이념, 교육목적, 그리고 교육목표는 다음 각 호와 같다. 교훈 : 사랑, 겸손, 봉사. 교육이념 : 대한민국의 교육이념과 기독교정신을 바탕으로 지성·인성·영성 교육을 통하여 세상을 변화시키는 지도자를 양성한다. 교육목적 : 기독교 정신에 기반 한 수준 높은 교수·연구를 통해 참된 인간성과 창조적 지성을 갖춘 지도적 인재를 양성하고 학술을 진흥하며 이를 통해 지역사회 및 국가의 발전과 인류 번영에 기여한다. 교육목표 : 기독교 정신, 학문적 탁월성, 세계시민 소양, 그리고 훌륭한 기독교적 인성, 특히 정직과 봉사의 희생정신을 겸비한 민족과 세계를 변화시키는 새로운 지도자를 배출한다.</code> |
—Loss: <code>MultipleNegativesRankingLoss</code> with these parameters:

json

  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }

Training Hyperparameters

Non-Default Hyperparameters

—eval_strategy: steps
—per_device_train_batch_size: 1
—per_device_eval_batch_size: 1
—num_train_epochs: 7
—fp16: True
—multi_dataset_batch_sampler: round_robin

All Hyperparameters

<details><summary>Click to expand</summary>

—overwrite_output_dir: False
—do_predict: False
—eval_strategy: steps
—prediction_loss_only: True
—per_device_train_batch_size: 1
—per_device_eval_batch_size: 1
—per_gpu_train_batch_size: None
—per_gpu_eval_batch_size: None
—gradient_accumulation_steps: 1
—eval_accumulation_steps: None
—torch_empty_cache_steps: None
—learning_rate: 5e-05
—weight_decay: 0.0
—adam_beta1: 0.9
—adam_beta2: 0.999
—adam_epsilon: 1e-08
—max_grad_norm: 1
—num_train_epochs: 7
—max_steps: -1
—lr_scheduler_type: linear
—lr_scheduler_kwargs: {}
—warmup_ratio: 0.0
—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: False
—fp16: True
—fp16_opt_level: O1
—half_precision_backend: auto
—bf16_full_eval: False
—fp16_full_eval: False
—tf32: None
—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: False
—ignore_data_skip: False
—fsdp: []
—fsdp_min_num_params: 0
—fsdp_config: {'minnumparams': 0, 'xla': False, 'xlafsdpv2': False, 'xlafsdpgrad_ckpt': False}
—fsdp_transformer_layer_cls_to_wrap: None
—accelerator_config: {'splitbatches': False, 'dispatchbatches': None, 'evenbatches': True, 'useseedablesampler': True, 'nonblocking': False, 'gradientaccumulationkwargs': None}
—deepspeed: None
—label_smoothing_factor: 0.0
—optim: adamw_torch
—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
—include_for_metrics: []
—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
—use_liger_kernel: False
—eval_use_gather_object: False
—average_tokens_across_devices: False
—prompts: None
—batch_sampler: batch_sampler
—multi_dataset_batch_sampler: round_robin

</details>

Training Logs

Epoch	Step
0.5714	20

Framework Versions

—Python: 3.10.13
—Sentence Transformers: 3.3.1
—Transformers: 4.46.2
—PyTorch: 2.0.1+cu118
—Accelerate: 0.34.2
—Datasets: 3.0.0
—Tokenizers: 0.20.1

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",
}

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}
}

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