--- language: - en license: apache-2.0 tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:9432 - loss:MatryoshkaLoss - loss:MultipleNegativesRankingLoss base_model: sentence-transformers/all-MiniLM-L6-v2 widget: - source_sentence: Atherosclerosis and coronary heart disease are examples of what type of body system disease? sentences: - Diseases of the cardiovascular system are common and may be life threatening. Examples include atherosclerosis and coronary heart disease. A healthy lifestyle can reduce the risk of such diseases developing. This includes avoiding smoking, getting regular physical activity, and maintaining a healthy percent of body fat. - Osmosis Osmosis is the diffusion of water through a semipermeable membrane according to the concentration gradient of water across the membrane. Whereas diffusion transports material across membranes and within cells, osmosis transports only water across a membrane and the membrane limits the diffusion of solutes in the water. Osmosis is a special case of diffusion. Water, like other substances, moves from an area of higher concentration to one of lower concentration. Imagine a beaker with a semipermeable membrane, separating the two sides or halves (Figure 3.21). On both sides of the membrane, the water level is the same, but there are different concentrations on each side of a dissolved substance, or solute, that cannot cross the membrane. If the volume of the water is the same, but the concentrations of solute are different, then there are also different concentrations of water, the solvent, on either side of the membrane. - Circadian rhythms are regular changes in biology or behavior that occur in a 24-hour cycle. In humans, for example, blood pressure and body temperature change in a regular way throughout each 24-hour day. Animals may eat and drink at certain times of day as well. Humans have daily cycles of behavior, too. Most people start to get sleepy after dark and have a hard time sleeping when it is light outside. In many species, including humans, circadian rhythms are controlled by a tiny structure called the biological clock . This structure is located in a gland at the base of the brain. The biological clock sends signals to the body. The signals cause regular changes in behavior and body processes. The amount of light entering the eyes helps control the biological clock. The clock causes changes that repeat every 24 hours. - source_sentence: How does a cell's membrane keep extracellular materials from mixing with it's internal components? sentences: - We know that the Universe is expanding. Astronomers have wondered if it is expanding fast enough to escape the pull of gravity. Would the Universe just expand forever? If it could not escape the pull of gravity, would it someday start to contract? This means it would eventually get squeezed together in a big crunch. This is the opposite of the Big Bang. - Physical properties that do not depend on the amount of substance present are called intensive properties . Intensive properties do not change with changes of size, shape, or scale. Examples of intensive properties are as follows in the Table below . - CHAPTER REVIEW 3.1 The Cell Membrane The cell membrane provides a barrier around the cell, separating its internal components from the extracellular environment. It is composed of a phospholipid bilayer, with hydrophobic internal lipid “tails” and hydrophilic external phosphate “heads. ” Various membrane proteins are scattered throughout the bilayer, both inserted within it and attached to it peripherally. The cell membrane is selectively permeable, allowing only a limited number of materials to diffuse through its lipid bilayer. All materials that cross the membrane do so using passive (non energy-requiring) or active (energy-requiring) transport processes. During passive transport, materials move by simple diffusion or by facilitated diffusion through the membrane, down their concentration gradient. Water passes through the membrane in a diffusion process called osmosis. During active transport, energy is expended to assist material movement across the membrane in a direction against their concentration gradient. Active transport may take place with the help of protein pumps or through the use of vesicles. - source_sentence: An infection may be intracellular or extracellular, depending on this? sentences: - '22.3 Magnetic Fields and Magnetic Field Lines • Magnetic fields can be pictorially represented by magnetic field lines, the properties of which are as follows: 1. The field is tangent to the magnetic field line. Field strength is proportional to the line density. Field lines cannot cross. Field lines are continuous loops.' - Figure 24.13 The lifecycle of an ascomycete is characterized by the production of asci during the sexual phase. The haploid phase is the predominant phase of the life cycle. - Caffeine is an example of a psychoactive drug. It is found in coffee and many other products (see Table below ). Caffeine is a central nervous system stimulant . Like other stimulant drugs, it makes you feel more awake and alert. Other psychoactive drugs include alcohol, nicotine, and marijuana. Each has a different effect on the central nervous system. Alcohol, for example, is a depressant . It has the opposite effects of a stimulant like caffeine. - source_sentence: What does water treatment do to water? sentences: - Some solutes, such as sodium acetate, do not recrystallize easily. Suppose an exactly saturated solution of sodium acetate is prepared at 50°C. As it cools back to room temperature, no crystals appear in the solution, even though the solubility of sodium acetate is lower at room temperature. A supersaturated solution is a solution that contains more than the maximum amount of solute that is capable of being dissolved at a given temperature. The recrystallization of the excess dissolved solute in a supersaturated solution can be initiated by the addition of a tiny crystal of solute, called a seed crystal. The seed crystal provides a nucleation site on which the excess dissolved crystals can begin to grow. Recrystallization from a supersaturated solution is typically very fast. - Figure 23.13, the esophagus runs a mainly straight route through the mediastinum of the thorax. To enter the abdomen, the esophagus penetrates the diaphragm through an opening called the esophageal hiatus. - Water treatment is a series of processes that remove unwanted substances from water. More processes are needed to purify water for drinking than for other uses. - source_sentence: 'There are only four possible bases that make up each dna nucleotide: adenine, guanine, thymine, and?' sentences: - Metamorphism. This long word means “to change form. “ A rock undergoes metamorphism if it is exposed to extreme heat and pressure within the crust. With metamorphism , the rock does not melt all the way. The rock changes due to heat and pressure. A metamorphic rock may have a new mineral composition and/or texture. - Forest and Kim Starr (Flickr:Starr Environmental). Secondary succession occurs when nature reclaims areas formerly occupied by life . CC BY 2.0. - 'The only difference between each nucleotide is the identity of the base. There are only four possible bases that make up each DNA nucleotide: adenine (A), guanine (G), thymine (T), and cytosine (C).' 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: MNLP M3 Encoder SciQA results: - task: type: information-retrieval name: Information Retrieval dataset: name: dim 384 type: dim_384 metrics: - type: cosine_accuracy@1 value: 0.6101048617731173 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8007626310772163 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8541468064823642 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9256434699714013 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.6101048617731173 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.2669208770257388 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.17082936129647283 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09256434699714014 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.6101048617731173 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8007626310772163 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8541468064823642 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9256434699714013 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.7675175612283535 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7170116664396936 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7197084605820631 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 256 type: dim_256 metrics: - type: cosine_accuracy@1 value: 0.5948522402287894 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.792183031458532 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8398474737845567 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9151572926596759 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.5948522402287894 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.2640610104861773 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.16796949475691134 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09151572926596759 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.5948522402287894 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.792183031458532 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8398474737845567 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9151572926596759 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.7548435122429773 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7035797509343749 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7070932589939358 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 192 type: dim_192 metrics: - type: cosine_accuracy@1 value: 0.5910390848427073 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.7778836987607245 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8360343183984748 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9046711153479504 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.5910390848427073 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.25929456625357483 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.16720686367969495 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09046711153479504 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.5910390848427073 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.7778836987607245 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8360343183984748 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9046711153479504 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.7477240665900656 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.6975449029309853 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7014228144337117 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 128 type: dim_128 metrics: - type: cosine_accuracy@1 value: 0.567206863679695 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.7616777883698761 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8265014299332698 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.8903717826501429 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.567206863679695 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.253892596123292 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.16530028598665394 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.08903717826501431 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.567206863679695 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.7616777883698761 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8265014299332698 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.8903717826501429 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.7273531110418706 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.6752920392815543 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.6794753898354032 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 96 type: dim_96 metrics: - type: cosine_accuracy@1 value: 0.5529075309818875 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.7416587225929456 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8093422306959008 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.8741658722592945 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.5529075309818875 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.24721957419764853 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.1618684461391802 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.08741658722592945 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.5529075309818875 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.7416587225929456 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8093422306959008 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.8741658722592945 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.7125237648315317 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.6608247461679306 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.6652525185575742 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 64 type: dim_64 metrics: - type: cosine_accuracy@1 value: 0.5166825548141086 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.7054337464251669 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.7673975214489991 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.8369876072449952 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.5166825548141086 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.23514458214172226 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.1534795042897998 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.08369876072449953 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.5166825548141086 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.7054337464251669 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.7673975214489991 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.8369876072449952 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.6755921916053389 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.6240088822309986 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.629350282837756 name: Cosine Map@100 --- # MNLP M3 Encoder SciQA This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) on the json dataset. It maps sentences & paragraphs to a 384-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:** [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) - **Maximum Sequence Length:** 256 tokens - **Output Dimensionality:** 384 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': 256, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 384, '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}) (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 = [ 'There are only four possible bases that make up each dna nucleotide: adenine, guanine, thymine, and?', 'The only difference between each nucleotide is the identity of the base. There are only four possible bases that make up each DNA nucleotide: adenine (A), guanine (G), thymine (T), and cytosine (C).', 'Metamorphism. This long word means “to change form. “ A rock undergoes metamorphism if it is exposed to extreme heat and pressure within the crust. With metamorphism , the rock does not melt all the way. The rock changes due to heat and pressure. A metamorphic rock may have a new mineral composition and/or texture.', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 384] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` ## Evaluation ### Metrics #### Information Retrieval * Datasets: `dim_384`, `dim_256`, `dim_192`, `dim_128`, `dim_96` and `dim_64` * Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | Metric | dim_384 | dim_256 | dim_192 | dim_128 | dim_96 | dim_64 | |:--------------------|:-----------|:-----------|:-----------|:-----------|:-----------|:-----------| | cosine_accuracy@1 | 0.6101 | 0.5949 | 0.591 | 0.5672 | 0.5529 | 0.5167 | | cosine_accuracy@3 | 0.8008 | 0.7922 | 0.7779 | 0.7617 | 0.7417 | 0.7054 | | cosine_accuracy@5 | 0.8541 | 0.8398 | 0.836 | 0.8265 | 0.8093 | 0.7674 | | cosine_accuracy@10 | 0.9256 | 0.9152 | 0.9047 | 0.8904 | 0.8742 | 0.837 | | cosine_precision@1 | 0.6101 | 0.5949 | 0.591 | 0.5672 | 0.5529 | 0.5167 | | cosine_precision@3 | 0.2669 | 0.2641 | 0.2593 | 0.2539 | 0.2472 | 0.2351 | | cosine_precision@5 | 0.1708 | 0.168 | 0.1672 | 0.1653 | 0.1619 | 0.1535 | | cosine_precision@10 | 0.0926 | 0.0915 | 0.0905 | 0.089 | 0.0874 | 0.0837 | | cosine_recall@1 | 0.6101 | 0.5949 | 0.591 | 0.5672 | 0.5529 | 0.5167 | | cosine_recall@3 | 0.8008 | 0.7922 | 0.7779 | 0.7617 | 0.7417 | 0.7054 | | cosine_recall@5 | 0.8541 | 0.8398 | 0.836 | 0.8265 | 0.8093 | 0.7674 | | cosine_recall@10 | 0.9256 | 0.9152 | 0.9047 | 0.8904 | 0.8742 | 0.837 | | **cosine_ndcg@10** | **0.7675** | **0.7548** | **0.7477** | **0.7274** | **0.7125** | **0.6756** | | cosine_mrr@10 | 0.717 | 0.7036 | 0.6975 | 0.6753 | 0.6608 | 0.624 | | cosine_map@100 | 0.7197 | 0.7071 | 0.7014 | 0.6795 | 0.6653 | 0.6294 | ## Training Details ### Training Dataset #### json * Dataset: json * Size: 9,432 training samples * Columns: anchor and positive * Approximate statistics based on the first 1000 samples: | | anchor | positive | |:--------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | anchor | positive | |:-------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | What is the term for atherosclerosis of arteries that supply the heart muscle? | Atherosclerosis of arteries that supply the heart muscle is called coronary heart disease . This disease may or may not have symptoms, such as chest pain. As the disease progresses, there is an increased risk of heart attack. A heart attack occurs when the blood supply to part of the heart muscle is blocked and cardiac muscle fibers die. Coronary heart disease is the leading cause of death of adults in the United States. | | What term describes a drug that has an effect on the central nervous system? | Caffeine is an example of a psychoactive drug. It is found in coffee and many other products (see Table below ). Caffeine is a central nervous system stimulant . Like other stimulant drugs, it makes you feel more awake and alert. Other psychoactive drugs include alcohol, nicotine, and marijuana. Each has a different effect on the central nervous system. Alcohol, for example, is a depressant . It has the opposite effects of a stimulant like caffeine. | | What scale is used to succinctly communicate the acidity or basicity of a solution? | The pH scale is used to succinctly communicate the acidity or basicity of a solution. | * Loss: [MatryoshkaLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 384, 256, 192, 128, 96, 64 ], "matryoshka_weights": [ 1, 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 - `tf32`: 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`: True - `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_384_cosine_ndcg@10 | dim_256_cosine_ndcg@10 | dim_192_cosine_ndcg@10 | dim_128_cosine_ndcg@10 | dim_96_cosine_ndcg@10 | dim_64_cosine_ndcg@10 | |:------:|:----:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:---------------------:| | 0.5424 | 10 | 22.4049 | - | - | - | - | - | - | | 1.0 | 19 | - | 0.7424 | 0.7315 | 0.7263 | 0.7093 | 0.6919 | 0.6575 | | 1.0542 | 20 | 16.6616 | - | - | - | - | - | - | | 1.5966 | 30 | 16.8367 | - | - | - | - | - | - | | 2.0 | 38 | - | 0.7612 | 0.7520 | 0.7431 | 0.7261 | 0.7097 | 0.6708 | | 2.1085 | 40 | 12.8169 | - | - | - | - | - | - | | 2.6508 | 50 | 13.7826 | - | - | - | - | - | - | | 3.0 | 57 | - | 0.7675 | 0.7548 | 0.7477 | 0.7274 | 0.7125 | 0.6756 | ### Framework Versions - Python: 3.12.8 - Sentence Transformers: 3.4.1 - Transformers: 4.51.3 - PyTorch: 2.5.1+cu124 - Accelerate: 1.3.0 - Datasets: 3.6.0 - 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} } ``` #### 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} } ```