--- language: - en license: apache-2.0 tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:5822 - loss:MatryoshkaLoss - loss:MultipleNegativesRankingLoss base_model: nomic-ai/nomic-embed-text-v1.5 widget: - source_sentence: "submitted to the CIA for each year.” Id. at 1–2. On July 22,\ \ 2010, the CIA responded to this \nrequest, stating “[w]e . . . have determined\ \ that our record systems are not configured in a way \nthat would allow us to\ \ perform a search reasonably calculated to lead to the responsive record \nwithout\ \ an unreasonable effort.” First Lutz Decl. Ex. L at 1, No. 11-444, ECF No. 20-3.\ \ As a" sentences: - How many instances of individual's names does the plaintiff point to? - What date did the CIA respond to the request? - What phrase does the Bar propose to delete references to in the Preamble to Chapter 4? - source_sentence: "City Department of Education, the self-represented plaintiff \n\ submitted a filing containing hallucinations. No. 24-cv-04232, \n \n20 \n2024\ \ WL 3460049, at *7 (S.D.N.Y. July 18, 2024) (unpublished \nopinion). The court\ \ noted that “[s]anctions may be imposed for \nsubmitting false and nonexistent\ \ legal authority to the [c]ourt.” Id. \nHowever, the court declined to impose\ \ sanctions due to the" sentences: - In which sections of their opposition does the plaintiff discuss the deliberative-process privilege? - Who submitted the filing containing hallucinations? - When did the plaintiff file a motion? - source_sentence: "§ 424 and Exemption 3; Exemption 5; and/or Exemption 6. See Second\ \ Williams Decl. Ex. A. \n120 \n \nTherefore, the Court need not decide whether\ \ the DIA has the independent authority to invoke \nthe National Security Act\ \ as an Exemption 3 withholding statute. \n3. \nODNI \nFinally, the plaintiff\ \ challenges the ODNI’s decision to withhold certain portions of e-" sentences: - How many counts did EPIC bring related to the APA? - Which organization's decision is being challenged by the plaintiff? - Does the Government agree with EPIC's claim about their Answer? - source_sentence: "confidentiality agreement/order, that remain following those discussions.\ \ This is a \nfinal report and notice of exceptions shall be filed within three\ \ days of the date of \nthis report, pursuant to Court of Chancery Rule 144(d)(2),\ \ given the expedited and \nsummary nature of Section 220 proceedings. \n \n\ \ \n \n \n \n \n \nRespectfully, \n \n \n \n \n \n \n \n \n/s/ Patricia W. Griffin" sentences: - Who signed this document? - Did Mr. Mooney allege that the video was altered or tampered with? - Did the plaintiff report the defendant at that time? - source_sentence: "such an argument, and she does not offer any case law, cites to\ \ secondary sources, dictionaries \nor grammatical texts, arguments by analogy,\ \ or other citations, except for the mere assertion \nthat defendant failed to\ \ move in a timely fashion after he was “on notice” of the ex parte order. \n\ A reviewing court is entitled to have issues clearly defined with relevant authority\ \ cited." sentences: - What page is Cross-MJAR's emphasis mentioned on? - What mere assertion does she make? - On what dates did the Commission meet in 2019? pipeline_tag: sentence-similarity library_name: sentence-transformers metrics: - cosine_accuracy@1 - cosine_accuracy@3 - cosine_accuracy@5 - cosine_accuracy@10 - cosine_precision@1 - cosine_precision@3 - cosine_precision@5 - cosine_precision@10 - cosine_recall@1 - cosine_recall@3 - cosine_recall@5 - cosine_recall@10 - cosine_ndcg@10 - cosine_mrr@10 - cosine_map@100 model-index: - name: nomic-embed-text-v1.5 results: - task: type: information-retrieval name: Information Retrieval dataset: name: dim 768 type: dim_768 metrics: - type: cosine_accuracy@1 value: 0.5486862442040186 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.5965996908809892 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.7017001545595054 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.7697063369397218 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.5486862442040186 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.5239567233384853 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.40989180834621336 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.24142194744976814 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.19049459041731065 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.5101751674394642 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.6503091190108191 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.7595311695002576 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.6615339195276682 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.6004440519123668 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.6427552042140723 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 512 type: dim_512 metrics: - type: cosine_accuracy@1 value: 0.5409582689335394 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.58887171561051 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.6924265842349304 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.7743431221020093 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.5409582689335394 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.5172591447707368 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.4034003091190108 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.24188562596599691 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.18740340030911898 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.5054095826893354 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.6411643482740855 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.7622359608449253 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.6576404555647709 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.5934416476533937 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.6355153178607286 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 256 type: dim_256 metrics: - type: cosine_accuracy@1 value: 0.508500772797527 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.5564142194744977 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.6707882534775889 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.7449768160741885 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.508500772797527 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.4873776403915508 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.38639876352395675 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.23122102009273574 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.17671303451828954 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.47707367336424517 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.6141164348274084 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.7257856774858321 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.6257588263652936 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.562961531856431 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.6091899586876254 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 128 type: dim_128 metrics: - type: cosine_accuracy@1 value: 0.45131375579598143 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.5054095826893354 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.58887171561051 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.6862442040185471 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.45131375579598143 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.437403400309119 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.3415765069551777 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.21298299845440496 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.15700669757856775 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.4282586295723854 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.5426326635754766 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.6720762493560021 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.5679548352076085 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.503881160913618 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.5511797935827811 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 64 type: dim_64 metrics: - type: cosine_accuracy@1 value: 0.35239567233384855 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.3894899536321484 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.47295208655332305 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.5641421947449768 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.35239567233384855 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.33900051519835134 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.26955177743431225 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.1723338485316847 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.12171561051004637 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.33217413704276144 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.4310922205048943 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.5446934569809376 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.45200452556542003 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.39659662422413555 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.44614347894124107 name: Cosine Map@100 --- # nomic-embed-text-v1.5 This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [nomic-ai/nomic-embed-text-v1.5](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5) on the json 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:** [nomic-ai/nomic-embed-text-v1.5](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5) - **Maximum Sequence Length:** 8192 tokens - **Output Dimensionality:** 768 dimensions - **Similarity Function:** Cosine Similarity - **Training Dataset:** - json - **Language:** en - **License:** apache-2.0 ### 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': 8192, 'do_lower_case': False}) with Transformer model: NomicBertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) ) ``` ## 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("Thejina/nomic-embed-text-finetuned") # Run inference sentences = [ 'such an argument, and she does not offer any case law, cites to secondary sources, dictionaries \nor grammatical texts, arguments by analogy, or other citations, except for the mere assertion \nthat defendant failed to move in a timely fashion after he was “on notice” of the ex parte order. \nA reviewing court is entitled to have issues clearly defined with relevant authority cited.', 'What mere assertion does she make?', "What page is Cross-MJAR's emphasis mentioned on?", ] 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 #### Information Retrieval * Dataset: `dim_768` * Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) with these parameters: ```json { "truncate_dim": 768 } ``` | Metric | Value | |:--------------------|:-----------| | cosine_accuracy@1 | 0.5487 | | cosine_accuracy@3 | 0.5966 | | cosine_accuracy@5 | 0.7017 | | cosine_accuracy@10 | 0.7697 | | cosine_precision@1 | 0.5487 | | cosine_precision@3 | 0.524 | | cosine_precision@5 | 0.4099 | | cosine_precision@10 | 0.2414 | | cosine_recall@1 | 0.1905 | | cosine_recall@3 | 0.5102 | | cosine_recall@5 | 0.6503 | | cosine_recall@10 | 0.7595 | | **cosine_ndcg@10** | **0.6615** | | cosine_mrr@10 | 0.6004 | | cosine_map@100 | 0.6428 | #### Information Retrieval * Dataset: `dim_512` * Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) with these parameters: ```json { "truncate_dim": 512 } ``` | Metric | Value | |:--------------------|:-----------| | cosine_accuracy@1 | 0.541 | | cosine_accuracy@3 | 0.5889 | | cosine_accuracy@5 | 0.6924 | | cosine_accuracy@10 | 0.7743 | | cosine_precision@1 | 0.541 | | cosine_precision@3 | 0.5173 | | cosine_precision@5 | 0.4034 | | cosine_precision@10 | 0.2419 | | cosine_recall@1 | 0.1874 | | cosine_recall@3 | 0.5054 | | cosine_recall@5 | 0.6412 | | cosine_recall@10 | 0.7622 | | **cosine_ndcg@10** | **0.6576** | | cosine_mrr@10 | 0.5934 | | cosine_map@100 | 0.6355 | #### Information Retrieval * Dataset: `dim_256` * Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) with these parameters: ```json { "truncate_dim": 256 } ``` | Metric | Value | |:--------------------|:-----------| | cosine_accuracy@1 | 0.5085 | | cosine_accuracy@3 | 0.5564 | | cosine_accuracy@5 | 0.6708 | | cosine_accuracy@10 | 0.745 | | cosine_precision@1 | 0.5085 | | cosine_precision@3 | 0.4874 | | cosine_precision@5 | 0.3864 | | cosine_precision@10 | 0.2312 | | cosine_recall@1 | 0.1767 | | cosine_recall@3 | 0.4771 | | cosine_recall@5 | 0.6141 | | cosine_recall@10 | 0.7258 | | **cosine_ndcg@10** | **0.6258** | | cosine_mrr@10 | 0.563 | | cosine_map@100 | 0.6092 | #### Information Retrieval * Dataset: `dim_128` * Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) with these parameters: ```json { "truncate_dim": 128 } ``` | Metric | Value | |:--------------------|:----------| | cosine_accuracy@1 | 0.4513 | | cosine_accuracy@3 | 0.5054 | | cosine_accuracy@5 | 0.5889 | | cosine_accuracy@10 | 0.6862 | | cosine_precision@1 | 0.4513 | | cosine_precision@3 | 0.4374 | | cosine_precision@5 | 0.3416 | | cosine_precision@10 | 0.213 | | cosine_recall@1 | 0.157 | | cosine_recall@3 | 0.4283 | | cosine_recall@5 | 0.5426 | | cosine_recall@10 | 0.6721 | | **cosine_ndcg@10** | **0.568** | | cosine_mrr@10 | 0.5039 | | cosine_map@100 | 0.5512 | #### Information Retrieval * Dataset: `dim_64` * Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) with these parameters: ```json { "truncate_dim": 64 } ``` | Metric | Value | |:--------------------|:----------| | cosine_accuracy@1 | 0.3524 | | cosine_accuracy@3 | 0.3895 | | cosine_accuracy@5 | 0.473 | | cosine_accuracy@10 | 0.5641 | | cosine_precision@1 | 0.3524 | | cosine_precision@3 | 0.339 | | cosine_precision@5 | 0.2696 | | cosine_precision@10 | 0.1723 | | cosine_recall@1 | 0.1217 | | cosine_recall@3 | 0.3322 | | cosine_recall@5 | 0.4311 | | cosine_recall@10 | 0.5447 | | **cosine_ndcg@10** | **0.452** | | cosine_mrr@10 | 0.3966 | | cosine_map@100 | 0.4461 | ## Training Details ### Training Dataset #### json * Dataset: json * Size: 5,822 training samples * Columns: positive and anchor * Approximate statistics based on the first 1000 samples: | | positive | anchor | |:--------|:------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | positive | anchor | |:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------| | functional test, too. Id. at 89–90. Still, the Court made clear that this functional test was “not
relevant.” Id. at 90. So, just as in Energy Research, its application of the functional test was
dicta. And because this discussion relied on the dicta from Energy Research, this was dicta
upon dicta.

The Government is thus imprecise when it asserts as the “law of the case” that the | What page is the functional test mentioned as 'not relevant'? | | authenticated through his testimony under Maryland Rule 5-901(b)(1) as a witness with
personal knowledge of the events.
- 6 -
The part of the video depicting the shooting was properly authenticated through
circumstantial evidence under Maryland Rule 5-901(b)(4), as there was sufficient
circumstantial evidence from which a reasonable juror could have inferred that the video | Which part of the video was authenticated? | | KLAN202300916




9
Los derechos morales, a su vez, están fundamentalmente
protegidos por la legislación estatal. Esta reconoce los derechos de
los autores como exclusivos de estos y los protege no solo en
beneficio propio, sino también de la sociedad por la contribución
social y cultural que históricamente se le ha reconocido a la | ¿En beneficio de quién se protegen los derechos de los autores? | * Loss: [MatryoshkaLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "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`: epoch - `per_device_train_batch_size`: 32 - `per_device_eval_batch_size`: 16 - `gradient_accumulation_steps`: 16 - `learning_rate`: 2e-05 - `num_train_epochs`: 4 - `lr_scheduler_type`: cosine - `warmup_ratio`: 0.1 - `bf16`: True - `load_best_model_at_end`: True - `optim`: adamw_torch_fused - `batch_sampler`: no_duplicates #### All Hyperparameters
Click to expand - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: epoch - `prediction_loss_only`: True - `per_device_train_batch_size`: 32 - `per_device_eval_batch_size`: 16 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 16 - `eval_accumulation_steps`: None - `torch_empty_cache_steps`: None - `learning_rate`: 2e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 4 - `max_steps`: -1 - `lr_scheduler_type`: cosine - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.1 - `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`: 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`: True - `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_fused - `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`: no_duplicates - `multi_dataset_batch_sampler`: proportional
### Training Logs | Epoch | Step | Training Loss | dim_768_cosine_ndcg@10 | dim_512_cosine_ndcg@10 | dim_256_cosine_ndcg@10 | dim_128_cosine_ndcg@10 | dim_64_cosine_ndcg@10 | |:----------:|:------:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:| | 0.8791 | 10 | 69.7578 | - | - | - | - | - | | 1.0 | 12 | - | 0.6178 | 0.6069 | 0.5742 | 0.5088 | 0.4115 | | 1.7033 | 20 | 28.4334 | - | - | - | - | - | | 2.0 | 24 | - | 0.6589 | 0.6509 | 0.6268 | 0.5616 | 0.4494 | | 2.5275 | 30 | 20.1123 | - | - | - | - | - | | 3.0 | 36 | - | 0.6621 | 0.6573 | 0.6263 | 0.5677 | 0.4508 | | 3.3516 | 40 | 16.5444 | - | - | - | - | - | | **3.7033** | **44** | **-** | **0.6615** | **0.6576** | **0.6258** | **0.568** | **0.452** | * The bold row denotes the saved checkpoint. ### 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: 3.6.0 - Tokenizers: 0.21.1 ## 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", } ``` #### 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} } ``` #### 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} } ```