chelleboyer/llm-mm-good-eb8e3f60-56f2-4729-8934-2428ca568d27

SentenceTransformer based on Snowflake/snowflake-arctic-embed-l

This is a sentence-transformers model finetuned from Snowflake/snowflake-arctic-embed-l. 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: Snowflake/snowflake-arctic-embed-l
• 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: BertModel 
  (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:

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("chelleboyer/llm-mm-good-eb8e3f60-56f2-4729-8934-2428ca568d27")
# Run inference
sentences = [
    'How do Dong et al. (2022) contribute to the understanding of in-context learning in their survey?',
    'Dong et\xa0al. (2024a)\n\nQingxiu Dong, Li Dong, Xingxing Zhang, Zhifang Sui, and Furu Wei. 2024a.\n\n\nSelf-Boosting Large Language Models with Synthetic Preference Data.\n\n\narXiv preprint arXiv:2410.06961 (2024).\n\n\n\n\n\n\nDong et\xa0al. (2022)\n\nQingxiu Dong, Lei Li, Damai Dai, Ce Zheng, Jingyuan Ma, Rui Li, Heming Xia, Jingjing Xu, Zhiyong Wu, Tianyu Liu, et\xa0al. 2022.\n\n\nA survey on in-context learning.\n\n\narXiv preprint arXiv:2301.00234 (2022).\n\n\n\n\n\n\nDong et\xa0al. (2024b)\n\nYijiang\xa0River Dong, Tiancheng Hu, and Nigel Collier. 2024b.\n\n\nCan LLM be a Personalized Judge?\n\n\narXiv preprint arXiv:2406.11657 (2024).\n\n\n\n\n\n\nDorner et\xa0al. (2024)\n\nFlorian\xa0E. Dorner, Vivian\xa0Y. Nastl, and Moritz Hardt. 2024.',
    'Additionally, the LLMAAA\xa0(Zhang et\xa0al., 2023a) framework incorporates an active learning strategy to efficiently select high-information samples for annotation, thereby mitigating the effects of noisy labels and reducing the reliance on costly human annotation. These approach not only enhance the performance of task-specific models but also offer new perspectives on the efficient application of LLMs in annotation workflows.',
]
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]

Evaluation

Metrics

Information Retrieval

• Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.92
cosine_accuracy@3	0.99
cosine_accuracy@5	1.0
cosine_accuracy@10	1.0
cosine_precision@1	0.92
cosine_precision@3	0.33
cosine_precision@5	0.2
cosine_precision@10	0.1
cosine_recall@1	0.92
cosine_recall@3	0.99
cosine_recall@5	1.0
cosine_recall@10	1.0
cosine_ndcg@10	0.9667
cosine_mrr@10	0.9553
cosine_map@100	0.9553

Training Details

Training Dataset

Unnamed Dataset

• Size: 1,334 training samples
• Columns: sentence_0 and sentence_1
• Approximate statistics based on the first 1000 samples:

  	sentence_0	sentence_1
  type	string	string
  details	min: 5 tokens mean: 23.14 tokens max: 69 tokens	min: 3 tokens mean: 132.04 tokens max: 306 tokens

• Samples:

  sentence_0	sentence_1
  What are the key components of the evaluation function ( E ) as described in the preliminaries section?	LLMs-as-Judges: A Comprehensive Survey on LLM-based Evaluation Methods 1 Introduction 2 PRELIMINARIES 2.1 Evaluation Function E𝐸Eitalic_E 2.2 Evaluation Input 2.2.1 Evaluation Type 𝒯𝒯\mathcal{T}caligraphic_T 2.2.2 Evaluation Criteria 𝒞𝒞\mathcal{C}caligraphic_C. 2.2.3 Evaluation References ℛℛ\mathcal{R}caligraphic_R. 2.3 Evaluation Output 3 Functionality 3.1 Performance Evaluation 3.1.1 Responses Evaluation 3.1.2 Model Evaluation 3.2 Model Enhancement 3.2.1 Reward Modeling During Training 3.2.2 Acting as Verifier During Inference 3.2.3 Feedback for Refinement 3.3 Data Construction 3.3.1 Data Annotation 3.3.2 Data Synthesize 4 Methodology
  How do LLMs contribute to model enhancement according to the functionalities outlined in the survey?	LLMs-as-Judges: A Comprehensive Survey on LLM-based Evaluation Methods 1 Introduction 2 PRELIMINARIES 2.1 Evaluation Function E𝐸Eitalic_E 2.2 Evaluation Input 2.2.1 Evaluation Type 𝒯𝒯\mathcal{T}caligraphic_T 2.2.2 Evaluation Criteria 𝒞𝒞\mathcal{C}caligraphic_C. 2.2.3 Evaluation References ℛℛ\mathcal{R}caligraphic_R. 2.3 Evaluation Output 3 Functionality 3.1 Performance Evaluation 3.1.1 Responses Evaluation 3.1.2 Model Evaluation 3.2 Model Enhancement 3.2.1 Reward Modeling During Training 3.2.2 Acting as Verifier During Inference 3.2.3 Feedback for Refinement 3.3 Data Construction 3.3.1 Data Annotation 3.3.2 Data Synthesize 4 Methodology
  What are the different approaches discussed under the Single-LLM System methodology?	4 Methodology 4.1 Single-LLM System 4.1.1 Prompt-based 4.1.2 Tuning-based 4.1.3 Post-processing 4.2 Multi-LLM System 4.2.1 Communication 4.2.2 Aggregation 4.3 Human-AI Collaboration System 5 Application 5.1 General 5.2 Multimodal 5.3 Medical 5.4 Legal 5.5 Financial 5.6 Education 5.7 Information Retrieval 5.8 Others 5.8.1 Soft Engineering 5.8.2 Biology 5.8.3 Social Science 6 Meta-evaluation 6.1 Benchmarks 6.1.1 Code Generation 6.1.2 Machine Translation 6.1.3 Text Summarization 6.1.4 Dialogue Generation 6.1.5 Automatic Story Generation 6.1.6 Values Alignment 6.1.7 Recommendation 6.1.8 Search 6.1.9 Comprehensive Data 6.2 Metric

• Loss: MatryoshkaLoss with these parameters:

  {
      "loss": "MultipleNegativesRankingLoss",
      "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: 50
• per_device_eval_batch_size: 50
• num_train_epochs: 10
• multi_dataset_batch_sampler: round_robin

All Hyperparameters

Click to expand

• overwrite_output_dir: False
• do_predict: False
• eval_strategy: steps
• prediction_loss_only: True
• per_device_train_batch_size: 50
• per_device_eval_batch_size: 50
• 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: 10
• 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: 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}
• tp_size: 0
• 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
• 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

Training Logs

Epoch	Step	cosine_ndcg@10
1.0	27	0.9647
1.8519	50	0.9685
2.0	54	0.9717
3.0	81	0.9717
3.7037	100	0.9778
4.0	108	0.9754
5.0	135	0.9699
5.5556	150	0.9699
6.0	162	0.9664
7.0	189	0.9630
7.4074	200	0.9667
8.0	216	0.9667
9.0	243	0.9667
9.2593	250	0.9667
10.0	270	0.9667

Framework Versions

• Python: 3.11.12
• Sentence Transformers: 3.4.1
• Transformers: 4.51.3
• PyTorch: 2.6.0+cu124
• Accelerate: 1.6.0
• Datasets: 3.6.0
• Tokenizers: 0.21.1

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",
}

MatryoshkaLoss

@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}
}

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}
}