--- language: - tr tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:482091 - loss:MatryoshkaLoss - loss:MultipleNegativesRankingLoss - loss:CoSENTLoss base_model: Alibaba-NLP/gte-multilingual-base widget: - source_sentence: Ya da dışarı çıkıp yürü ya da biraz koşun. Bunu düzenli olarak yapmıyorum ama Washington bunu yapmak için harika bir yer. sentences: - “Washington's yürüyüş ya da koşu için harika bir yer.” - H-2A uzaylılar Amerika Birleşik Devletleri'nde zaman kısa süreleri var. - “Washington'da düzenli olarak yürüyüşe ya da koşuya çıkıyorum.” - source_sentence: Orta yaylalar ve güney kıyıları arasındaki kontrast daha belirgin olamazdı. sentences: - İşitme Yardımı Uyumluluğu Müzakere Kuralları Komitesi, Federal İletişim Komisyonu'nun bir ürünüdür. - Dağlık ve sahil arasındaki kontrast kolayca işaretlendi. - Kontrast işaretlenemedi. - source_sentence: Bir 1997 Henry J. Kaiser Aile Vakfı anket yönetilen bakım planlarında Amerikalılar temelde kendi bakımı ile memnun olduğunu bulundu. sentences: - Kaplanları takip ederken çok sessiz olmalısın. - Henry Kaiser vakfı insanların sağlık hizmetlerinden hoşlandığını gösteriyor. - Henry Kaiser Vakfı insanların sağlık hizmetlerinden nefret ettiğini gösteriyor. - source_sentence: Eminim yapmışlardır. sentences: - Eminim öyle yapmışlardır. - Batı Teksas'ta 100 10 dereceydi. - Eminim yapmamışlardır. - source_sentence: Ve gerçekten, baba haklıydı, oğlu zaten her şeyi tecrübe etmişti, her şeyi denedi ve daha az ilgileniyordu. sentences: - Oğlu her şeye olan ilgisini kaybediyordu. - Pek bir şey yapmadım. - Baba oğlunun tecrübe için hala çok şey olduğunu biliyordu. datasets: - emrecan/all-nli-tr pipeline_tag: sentence-similarity library_name: sentence-transformers metrics: - cosine_accuracy - pearson_cosine - spearman_cosine model-index: - name: SentenceTransformer based on Alibaba-NLP/gte-multilingual-base results: - task: type: triplet name: Triplet dataset: name: all nli tr test type: all-nli-tr-test metrics: - type: cosine_accuracy value: 0.8966145437983908 name: Cosine Accuracy - type: cosine_accuracy value: 0.9351753453772582 name: Cosine Accuracy - task: type: semantic-similarity name: Semantic Similarity dataset: name: sts test type: sts-test metrics: - type: pearson_cosine value: 0.8043925123766598 name: Pearson Cosine - type: spearman_cosine value: 0.804133282756889 name: Spearman Cosine - type: pearson_cosine value: 0.8133873820848544 name: Pearson Cosine - type: spearman_cosine value: 0.8199552151367876 name: Spearman Cosine - task: type: semantic-similarity name: Semantic Similarity dataset: name: sts22 test type: sts22-test metrics: - type: pearson_cosine value: 0.647912337747937 name: Pearson Cosine - type: spearman_cosine value: 0.6694072470896322 name: Spearman Cosine - type: pearson_cosine value: 0.6514085062457564 name: Pearson Cosine - type: spearman_cosine value: 0.6827342891126081 name: Spearman Cosine - task: type: semantic-similarity name: Semantic Similarity dataset: name: sts dev gte multilingual base type: sts-dev-gte-multilingual-base metrics: - type: pearson_cosine value: 0.838717139426684 name: Pearson Cosine - type: spearman_cosine value: 0.8428367492381358 name: Spearman Cosine - task: type: semantic-similarity name: Semantic Similarity dataset: name: sts test gte multilingual base type: sts-test-gte-multilingual-base metrics: - type: pearson_cosine value: 0.8133873820848544 name: Pearson Cosine - type: spearman_cosine value: 0.8199552151367876 name: Spearman Cosine - task: type: semantic-similarity name: Semantic Similarity dataset: name: stsb dev 768 type: stsb-dev-768 metrics: - type: pearson_cosine value: 0.870311456444647 name: Pearson Cosine - type: spearman_cosine value: 0.8747522169942328 name: Spearman Cosine - task: type: semantic-similarity name: Semantic Similarity dataset: name: stsb dev 512 type: stsb-dev-512 metrics: - type: pearson_cosine value: 0.8696934286998554 name: Pearson Cosine - type: spearman_cosine value: 0.8753487201891684 name: Spearman Cosine - task: type: semantic-similarity name: Semantic Similarity dataset: name: stsb dev 256 type: stsb-dev-256 metrics: - type: pearson_cosine value: 0.8644706498119142 name: Pearson Cosine - type: spearman_cosine value: 0.873468734899321 name: Spearman Cosine - task: type: semantic-similarity name: Semantic Similarity dataset: name: stsb dev 128 type: stsb-dev-128 metrics: - type: pearson_cosine value: 0.8591309130178328 name: Pearson Cosine - type: spearman_cosine value: 0.8700377378574327 name: Spearman Cosine - task: type: semantic-similarity name: Semantic Similarity dataset: name: stsb dev 64 type: stsb-dev-64 metrics: - type: pearson_cosine value: 0.8479124810212979 name: Pearson Cosine - type: spearman_cosine value: 0.8655596653561272 name: Spearman Cosine - task: type: semantic-similarity name: Semantic Similarity dataset: name: stsb test 768 type: stsb-test-768 metrics: - type: pearson_cosine value: 0.8455412308380735 name: Pearson Cosine - type: spearman_cosine value: 0.8535290217691063 name: Spearman Cosine - task: type: semantic-similarity name: Semantic Similarity dataset: name: stsb test 512 type: stsb-test-512 metrics: - type: pearson_cosine value: 0.8464773608783734 name: Pearson Cosine - type: spearman_cosine value: 0.8553900248212041 name: Spearman Cosine - task: type: semantic-similarity name: Semantic Similarity dataset: name: stsb test 256 type: stsb-test-256 metrics: - type: pearson_cosine value: 0.8443046458551826 name: Pearson Cosine - type: spearman_cosine value: 0.8550098621393595 name: Spearman Cosine - task: type: semantic-similarity name: Semantic Similarity dataset: name: stsb test 128 type: stsb-test-128 metrics: - type: pearson_cosine value: 0.8363964421208214 name: Pearson Cosine - type: spearman_cosine value: 0.8511193715667303 name: Spearman Cosine - task: type: semantic-similarity name: Semantic Similarity dataset: name: stsb test 64 type: stsb-test-64 metrics: - type: pearson_cosine value: 0.8235450515966374 name: Pearson Cosine - type: spearman_cosine value: 0.8460761238725121 name: Spearman Cosine --- # SentenceTransformer based on Alibaba-NLP/gte-multilingual-base This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Alibaba-NLP/gte-multilingual-base](https://huggingface.co/Alibaba-NLP/gte-multilingual-base) on the [all-nli-tr](https://huggingface.co/datasets/emrecan/all-nli-tr) dataset. 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](https://huggingface.co/Alibaba-NLP/gte-multilingual-base) - **Maximum Sequence Length:** 512 tokens - **Output Dimensionality:** 768 dimensions - **Similarity Function:** Cosine Similarity - **Training Dataset:** - [all-nli-tr](https://huggingface.co/datasets/emrecan/all-nli-tr) - **Language:** tr ### Model Sources - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### 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: ```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 = [ 'Ve gerçekten, baba haklıydı, oğlu zaten her şeyi tecrübe etmişti, her şeyi denedi ve daha az ilgileniyordu.', 'Oğlu her şeye olan ilgisini kaybediyordu.', 'Baba oğlunun tecrübe için hala çok şey olduğunu biliyordu.', ] 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] ``` ## Evaluation ### Metrics #### Triplet * Dataset: `all-nli-tr-test` * Evaluated with [TripletEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator) | Metric | Value | |:--------------------|:-----------| | **cosine_accuracy** | **0.8966** | #### Semantic Similarity * Datasets: `sts-test`, `sts22-test`, `sts-dev-gte-multilingual-base`, `sts-test-gte-multilingual-base`, `sts-test`, `sts22-test`, `stsb-dev-768`, `stsb-dev-512`, `stsb-dev-256`, `stsb-dev-128`, `stsb-dev-64`, `stsb-test-768`, `stsb-test-512`, `stsb-test-256`, `stsb-test-128` and `stsb-test-64` * Evaluated with [EmbeddingSimilarityEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) | Metric | sts-test | sts22-test | sts-dev-gte-multilingual-base | sts-test-gte-multilingual-base | stsb-dev-768 | stsb-dev-512 | stsb-dev-256 | stsb-dev-128 | stsb-dev-64 | stsb-test-768 | stsb-test-512 | stsb-test-256 | stsb-test-128 | stsb-test-64 | |:--------------------|:---------|:-----------|:------------------------------|:-------------------------------|:-------------|:-------------|:-------------|:-------------|:------------|:--------------|:--------------|:--------------|:--------------|:-------------| | pearson_cosine | 0.8134 | 0.6514 | 0.8387 | 0.8134 | 0.8703 | 0.8697 | 0.8645 | 0.8591 | 0.8479 | 0.8455 | 0.8465 | 0.8443 | 0.8364 | 0.8235 | | **spearman_cosine** | **0.82** | **0.6827** | **0.8428** | **0.82** | **0.8748** | **0.8753** | **0.8735** | **0.87** | **0.8656** | **0.8535** | **0.8554** | **0.855** | **0.8511** | **0.8461** | #### Triplet * Dataset: `all-nli-tr-test` * Evaluated with [TripletEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator) | Metric | Value | |:--------------------|:-----------| | **cosine_accuracy** | **0.9352** | ## Training Details ### Training Dataset #### all-nli-tr * Dataset: [all-nli-tr](https://huggingface.co/datasets/emrecan/all-nli-tr) at [daeabfb](https://huggingface.co/datasets/emrecan/all-nli-tr/tree/daeabfbc01f82757ab998bd23ce0ddfceaa5e24d) * Size: 482,091 training samples * Columns: sentence1, sentence2, and score * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | score | |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------| | type | string | string | float | | details | | | | * Samples: | sentence1 | sentence2 | score | |:----------------------------------------------------------|:-------------------------------------------------------------------|:-----------------| | Bir uçak kalkıyor. | Bir hava uçağı kalkıyor. | 5.0 | | Bir adam büyük bir flüt çalıyor. | Bir adam flüt çalıyor. | 3.8 | | Bir adam pizzaya rendelenmiş peynir yayıyor. | Bir adam pişmemiş pizzaya rendelenmiş peynir yayıyor. | 3.8 | * Loss: [MatryoshkaLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "CoSENTLoss", "matryoshka_dims": [ 768, 512, 256, 128, 64 ], "matryoshka_weights": [ 1, 1, 1, 1, 1 ], "n_dims_per_step": -1 } ``` ### Evaluation Dataset #### all-nli-tr * Dataset: [all-nli-tr](https://huggingface.co/datasets/emrecan/all-nli-tr) at [daeabfb](https://huggingface.co/datasets/emrecan/all-nli-tr/tree/daeabfbc01f82757ab998bd23ce0ddfceaa5e24d) * Size: 6,567 evaluation samples * Columns: sentence1, sentence2, and score * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | score | |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:--------------------------------------------------------------| | type | string | string | float | | details | | | | * Samples: | sentence1 | sentence2 | score | |:---------------------------------------------|:----------------------------------------------------|:------------------| | Şapkalı bir adam dans ediyor. | Sert şapka takan bir adam dans ediyor. | 5.0 | | Küçük bir çocuk ata biniyor. | Bir çocuk ata biniyor. | 4.75 | | Bir adam yılana fare yediriyor. | Adam yılana fare yediriyor. | 5.0 | * Loss: [MatryoshkaLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "CoSENTLoss", "matryoshka_dims": [ 768, 512, 256, 128, 64 ], "matryoshka_weights": [ 1, 1, 1, 1, 1 ], "n_dims_per_step": -1 } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 32 - `per_device_eval_batch_size`: 32 - `learning_rate`: 1e-05 - `weight_decay`: 0.01 - `num_train_epochs`: 10 - `warmup_ratio`: 0.1 - `warmup_steps`: 144 - `bf16`: True #### All Hyperparameters
Click to expand - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: steps - `prediction_loss_only`: True - `per_device_train_batch_size`: 32 - `per_device_eval_batch_size`: 32 - `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`: 1e-05 - `weight_decay`: 0.01 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 10 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.1 - `warmup_steps`: 144 - `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`: 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`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': 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`: None - `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`: proportional
### Training Logs | Epoch | Step | Training Loss | Validation Loss | all-nli-tr-test_cosine_accuracy | sts-test_spearman_cosine | sts22-test_spearman_cosine | sts-dev-gte-multilingual-base_spearman_cosine | sts-test-gte-multilingual-base_spearman_cosine | stsb-dev-768_spearman_cosine | stsb-dev-512_spearman_cosine | stsb-dev-256_spearman_cosine | stsb-dev-128_spearman_cosine | stsb-dev-64_spearman_cosine | stsb-test-768_spearman_cosine | stsb-test-512_spearman_cosine | stsb-test-256_spearman_cosine | stsb-test-128_spearman_cosine | stsb-test-64_spearman_cosine | |:------:|:----:|:-------------:|:---------------:|:-------------------------------:|:------------------------:|:--------------------------:|:---------------------------------------------:|:----------------------------------------------:|:----------------------------:|:----------------------------:|:----------------------------:|:----------------------------:|:---------------------------:|:-----------------------------:|:-----------------------------:|:-----------------------------:|:-----------------------------:|:----------------------------:| | 0 | 0 | - | - | 0.8966 | 0.8041 | 0.6694 | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1327 | 1000 | 2.5299 | 3.3893 | - | - | - | 0.8318 | - | - | - | - | - | - | - | - | - | - | - | | 0.2655 | 2000 | 2.1132 | 3.3050 | - | - | - | 0.8345 | - | - | - | - | - | - | - | - | - | - | - | | 0.3982 | 3000 | 5.1488 | 2.7752 | - | - | - | 0.8481 | - | - | - | - | - | - | - | - | - | - | - | | 0.5310 | 4000 | 5.4103 | 2.7242 | - | - | - | 0.8445 | - | - | - | - | - | - | - | - | - | - | - | | 0.6637 | 5000 | 5.1896 | 2.6701 | - | - | - | 0.8451 | - | - | - | - | - | - | - | - | - | - | - | | 0.7965 | 6000 | 5.0105 | 2.6489 | - | - | - | 0.8431 | - | - | - | - | - | - | - | - | - | - | - | | 0.9292 | 7000 | 5.1059 | 2.6114 | - | - | - | 0.8428 | - | - | - | - | - | - | - | - | - | - | - | | 1.0 | 7533 | - | - | 0.9352 | 0.8200 | 0.6827 | - | 0.8200 | - | - | - | - | - | - | - | - | - | - | | 1.1111 | 200 | 34.2828 | 29.8737 | - | - | - | - | - | 0.8671 | 0.8671 | 0.8639 | 0.8606 | 0.8546 | - | - | - | - | - | | 2.2222 | 400 | 28.038 | 28.8915 | - | - | - | - | - | 0.8740 | 0.8742 | 0.8720 | 0.8691 | 0.8648 | - | - | - | - | - | | 3.3333 | 600 | 27.3829 | 29.3391 | - | - | - | - | - | 0.8747 | 0.8751 | 0.8728 | 0.8699 | 0.8653 | - | - | - | - | - | | 4.4444 | 800 | 26.807 | 30.0090 | - | - | - | - | - | 0.8756 | 0.8761 | 0.8741 | 0.8710 | 0.8665 | - | - | - | - | - | | 5.5556 | 1000 | 26.4543 | 30.5886 | - | - | - | - | - | 0.8753 | 0.8757 | 0.8739 | 0.8705 | 0.8662 | - | - | - | - | - | | 6.6667 | 1200 | 26.0413 | 31.3750 | - | - | - | - | - | 0.8744 | 0.8751 | 0.8730 | 0.8698 | 0.8655 | - | - | - | - | - | | 7.7778 | 1400 | 25.8221 | 31.6515 | - | - | - | - | - | 0.8752 | 0.8758 | 0.8739 | 0.8706 | 0.8661 | - | - | - | - | - | | 8.8889 | 1600 | 25.6656 | 31.9805 | - | - | - | - | - | 0.8746 | 0.8752 | 0.8733 | 0.8700 | 0.8655 | - | - | - | - | - | | 10.0 | 1800 | 25.5355 | 32.0454 | - | - | - | - | - | 0.8748 | 0.8753 | 0.8735 | 0.8700 | 0.8656 | 0.8535 | 0.8554 | 0.8550 | 0.8511 | 0.8461 | ### Framework Versions - Python: 3.11.11 - Sentence Transformers: 3.3.1 - Transformers: 4.49.0.dev0 - PyTorch: 2.5.1+cu121 - Accelerate: 1.2.1 - Datasets: 3.2.0 - Tokenizers: 0.21.0 ## 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", } ``` #### MatryoshkaLoss ```bibtex @misc{kusupati2024matryoshka, title={Matryoshka Representation Learning}, author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi}, year={2024}, eprint={2205.13147}, archivePrefix={arXiv}, primaryClass={cs.LG} } ``` #### CoSENTLoss ```bibtex @online{kexuefm-8847, title={CoSENT: A more efficient sentence vector scheme than Sentence-BERT}, author={Su Jianlin}, year={2022}, month={Jan}, url={https://kexue.fm/archives/8847}, } ```