chelleboyer/llm-evals-2-79b954ef-4798-4994-be72-a88d46b8ecca

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-evals-2-79b954ef-4798-4994-be72-a88d46b8ecca")
# Run inference
sentences = [
    'What is the main contribution of Kwiatkowski et al. [2019] in the field of question answering research?',
    'Kwiatkowski et\xa0al. [2019]\n\nT.\xa0Kwiatkowski, J.\xa0Palomaki, O.\xa0Redfield, M.\xa0Collins, A.\xa0Parikh, C.\xa0Alberti, D.\xa0Epstein, I.\xa0Polosukhin, M.\xa0Kelcey, J.\xa0Devlin, K.\xa0Lee, K.\xa0N. Toutanova, L.\xa0Jones, M.-W. Chang, A.\xa0Dai, J.\xa0Uszkoreit, Q.\xa0Le, and S.\xa0Petrov.\n\n\nNatural questions: a benchmark for question answering research.\n\n\nTransactions of the Association of Computational Linguistics, 2019.\n\n\n\n\nLaurer et\xa0al. [2022]\n\nM.\xa0Laurer, W.\xa0van Atteveldt, A.\xa0Casas, and K.\xa0Welbers.',
    'The sentence_support_information field is a list of objects, one for each sentence\nin the response. Each object MUST have the following fields:\n- response_sentence_key: a string identifying the sentence in the response.\nThis key is the same as the one used in the response above.\n- explanation: a string explaining why the sentence is or is not supported by the\ndocuments.\n- supporting_sentence_keys: keys (e.g. ’0a’) of sentences from the documents that',
]
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	0.8571
cosine_accuracy@3	0.9643
cosine_accuracy@5	1.0
cosine_accuracy@10	1.0
cosine_precision@1	0.8571
cosine_precision@3	0.3214
cosine_precision@5	0.2
cosine_precision@10	0.1
cosine_recall@1	0.8571
cosine_recall@3	0.9643
cosine_recall@5	1.0
cosine_recall@10	1.0
cosine_ndcg@10	0.9386
cosine_mrr@10	0.9179
cosine_map@100	0.9179

Training Details

Training Dataset

Unnamed Dataset

• Size: 400 training samples
• Columns: sentence_0 and sentence_1
• Approximate statistics based on the first 400 samples:

  	sentence_0	sentence_1
  type	string	string
  details	min: 3 tokens mean: 21.42 tokens max: 53 tokens	min: 3 tokens mean: 93.8 tokens max: 200 tokens

• Samples:

  sentence_0	sentence_1
  What are the key components and criteria used in the TRACe Evaluation Framework within RAGBench?	RAGBench: Explainable Benchmark for Retrieval-Augmented Generation Systems 1 Introduction 2 Related Work RAG evaluation Finetuned RAG evaluation models 3 RAGBench Construction 3.1 Component Datasets Source Domains Context Token Length Task Types Question Sources Response Generation Data Splits 3.2 TRACe Evaluation Framework Definitions Context Relevance Context Utilization Completeness Adherence 3.3 RAGBench Statistics 3.4 LLM annotator
  How does RAGBench utilize component datasets to construct a benchmark for Retrieval-Augmented Generation systems?	RAGBench: Explainable Benchmark for Retrieval-Augmented Generation Systems 1 Introduction 2 Related Work RAG evaluation Finetuned RAG evaluation models 3 RAGBench Construction 3.1 Component Datasets Source Domains Context Token Length Task Types Question Sources Response Generation Data Splits 3.2 TRACe Evaluation Framework Definitions Context Relevance Context Utilization Completeness Adherence 3.3 RAGBench Statistics 3.4 LLM annotator
  What are the key components and findings discussed in the RAGBench Statistics and Case Study sections?	3.3 RAGBench Statistics 3.4 LLM annotator Alignment with Human Judgements 3.5 RAG Case Study 4 Experiments 4.1 LLM Judge 4.2 Fine-tuned Judge 4.3 Evaluation 5 Results Estimating Context Relevance is Difficult 6 Conclusion 7 Appendix 7.1 RAGBench Code and Data 7.2 RAGBench Dataset Details PubMedQA [14] CovidQA-RAG HotpotQA [42] MS Marco [28] CUAD [12] DelucionQA [33] EManual [27] TechQA [3] FinQA [6] TAT-QA [47] HAGRID [15] ExpertQA [25]

• 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: 5
• per_device_eval_batch_size: 5
• 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: 5
• per_device_eval_batch_size: 5
• 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	Training Loss	cosine_ndcg@10
0.625	50	-	0.9517
1.0	80	-	0.9649
1.25	100	-	0.9649
1.875	150	-	0.9517
2.0	160	-	0.9517
2.5	200	-	0.9386
3.0	240	-	0.9386
3.125	250	-	0.9517
3.75	300	-	0.9386
4.0	320	-	0.9517
4.375	350	-	0.9517
5.0	400	-	0.9517
5.625	450	-	0.9517
6.0	480	-	0.9401
6.25	500	0.3877	0.9401
6.875	550	-	0.9386
7.0	560	-	0.9386
7.5	600	-	0.9401
8.0	640	-	0.9401
8.125	650	-	0.9401
8.75	700	-	0.9386
9.0	720	-	0.9386
9.375	750	-	0.9386
10.0	800	-	0.9386

Framework Versions

• Python: 3.11.12
• Sentence Transformers: 4.1.0
• Transformers: 4.51.3
• PyTorch: 2.6.0+cu124
• Accelerate: 1.6.0
• Datasets: 2.14.4
• 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}
}