seongil-dn/gte-further-filtered-neg5

SentenceTransformer based on Alibaba-NLP/gte-multilingual-base

This is a sentence-transformers model finetuned from Alibaba-NLP/gte-multilingual-base. It maps sentences & paragraphs to a 768-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: Alibaba-NLP/gte-multilingual-base <!-- at revision 7fc06782350c1a83f88b15dd4b38ef853d3b8503 -->
• —Maximum Sequence Length: 512 tokens
• —Output Dimensionality: 768 tokens
• —Similarity Function: Cosine Similarity <!-- - Training Dataset: Unknown --> <!-- - Language: Unknown --> <!-- - License: Unknown -->

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: NewModel 
  (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()
)

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("seongil-dn/gte-further-filtered-neg5")
# Run inference
sentences = [
    '유성체 흐름은 어떻게 분포되어 있나요?',
    '유성체 흐름은 대체로 모혜성의 공전궤도를 중심으로 원통형으로 분포되어 있다. 유성체의 밀도는 모혜성의 공전 궤도로 갈수록 높아지며, 지구가 이러한 유성체 흐름을 관통할 때, 중심에 다가갈수록 더 많은 유성체가 지구 대기 속으로 돌입하게 된다. 따라서 한 유성우가 나타날 때는 매일 나타나는 유성의 개수가 증가하다가 감소하는 경향을 띤다. 관측적으로 지수함수적으로 증가하다가 지수함수적으로 감소하는 경향을 보인다. 한 유성우가 나타나는 시기의 유성개수의 변화는, 어떤 시점 formula_1에서 formula_2 와 같이 나타낼 수 있다. 유성의 개수는 formula_3일 때 최대가 되는데, 이것을 극대기라고 한다. 또한 formula_4의 시간 규모는 유성의 개수가 확연하게 변하는 시간 규모에 해당한다. 이른바 지수함수적 시간척도(e-folding time scale)이라고 하는 것이다. 단순히 나타나는 유성의 개수를 세기만 해도 이러한 값들은 측정할 수 있으며, 이로부터 지구 공전 궤도상에 놓여 있는 유성체 흐름의 분포를 자세히 연구할 수 있다.',
    '유성체는 대부분 혜성에서 떨어져 나온 부스러기이며, 일부는 소행성에서 떨어져 나온 부스러기도 있다. 유성체는 혜성이 해에 가까이 올 때마다 방출되는데, 해에 접근한 혜성의 속도는 보통 수 십 km/s를 넘는다. 유성체들이 혜성에서 떨어져 나올 때, 방출 속도가 조금씩 다르고 혜성이 또한 자전하므로 유성체들의 속도 성분은 혜성의 속도와 약간씩 차이가 생기게 된다. 그러나 그 양은 혜성의 속도에 비해 아주 작다. 그러나 이 작은 속도 차이 때문에 유성체들은 대체로 혜성의 궤도를 따라 운동을 하되 약간씩 다른 궤도를 돌게 되어, 마침내 혜성에서 나온 유성체들은 혜성의 공전 궤도를 따라 띠를 형성하게 된다. 더군다나 한번 방출된 유성체는 주로 목성과 해의 인력을 받게 되므로 띠는 점점 더 넓어지고 균질하게 된다. 이것을 유성체 흐름(meteoroid stream)이라고 한다. 지구가 유성체 흐름을 휩쓸고 지나갈 때 유성우가 일어난다.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# 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 Hyperparameters

Non-Default Hyperparameters

• —per_device_train_batch_size: 64
• —learning_rate: 7e-05
• —adam_epsilon: 1e-07
• —warmup_ratio: 0.05
• —bf16: True
• —batch_sampler: no_duplicates

All Hyperparameters

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

• —overwrite_output_dir: False
• —do_predict: False
• —eval_strategy: no
• —prediction_loss_only: True
• —per_device_train_batch_size: 64
• —per_device_eval_batch_size: 8
• —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: 7e-05
• —weight_decay: 0.0
• —adam_beta1: 0.9
• —adam_beta2: 0.999
• —adam_epsilon: 1e-07
• —max_grad_norm: 1.0
• —num_train_epochs: 3
• —max_steps: -1
• —lr_scheduler_type: linear
• —lr_scheduler_kwargs: {}
• —warmup_ratio: 0.05
• —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: True
• —fp16: False
• —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: True
• —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
• —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
• —eval_use_gather_object: False
• —batch_sampler: no_duplicates
• —multi_dataset_batch_sampler: proportional

</details>

Training Logs

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

</details>

Framework Versions

• —Python: 3.10.13
• —Sentence Transformers: 3.2.1
• —Transformers: 4.44.2
• —PyTorch: 2.4.0+cu121
• —Accelerate: 1.1.1
• —Datasets: 2.21.0
• —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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