| --- |
| tags: |
| - sentence-transformers |
| - sentence-similarity |
| - feature-extraction |
| - generated_from_trainer |
| - dataset_size:53851 |
| - loss:MultipleNegativesRankingLoss |
| base_model: BAAI/bge-base-en-v1.5 |
| widget: |
| - source_sentence: A certain junior class has 1000 students and a certain senior class |
| has 900 students. Among these students, there are 60 siblings pairs each consisting |
| of 1 junior and 1 senior. If 1 student is to be selected at random from each class, |
| what is the probability that the 2 students selected will be a sibling pair? |
| sentences: |
| - Let's see Pick 60/1000 first Then we can only pick 1 other pair from the 800 So |
| total will be 60 / 900 *1000 Simplify and you get 2/30000 |
| - To maximize number of hot dogs with 300$ Total number of hot dogs bought in 250-pack |
| = 22.95*13 =298.35$ Amount remaining = 300 - 298.35 = 1.65$ This amount is too |
| less to buy any 8- pack . Greatest number of hot dogs one can buy with 300 $ = |
| 250*13 = 3250 |
| - artificial leg |
| - source_sentence: A stock trader originally bought 300 shares of stock from a company |
| at a total cost of m dollars. If each share was sold at 80% above the original |
| cost per share of stock, then interns of m for how many dollars was each share |
| sold? |
| sentences: |
| - Let Cost of 300 shares be $ 3000 So, Cost of 1 shares be $ 10 =>m/300 Selling |
| price per share = (100+80)/100 * m/300 Or, Selling price per share = 9/5 * m/300 |
| => 9m/1500 |
| - The prognostic value of p53 nuclear accumulation in gastric cancer is still unclear, |
| as shown by the discordant results still reported in the literature. In this study, |
| we evaluated the correlation between p53 accumulation and long-term survival of |
| patients resected for intestinal and diffuse-type gastric cancer. Eighty-three |
| patients with carcinoma of the intestinal type and 53 patients with carcinoma |
| of the diffuse type were included in the study. Immunohistochemical staining of |
| the paraffin sections was performed by using monoclonal antibody DO1; cases were |
| considered positive when nuclear immunostaining was observed in 10% or more of |
| the tumor cells. Prognostic significance of different variables was investigated |
| by univariate and multivariate analysis. p53 positivity was found in 51.8% of |
| intestinal-type and 50.9% of diffuse-type cases. No significant correlation between |
| the rate of p53 overexpression and age, sex, tumor location, tumor size, depth |
| of invasion, lymph node involvement, distant metastases, and surgical radicality |
| was found in the two groups of patients. A statistically significant difference |
| in survival rate was observed between p53-negative and p53-positive cases in the |
| intestinal type (P < .05), confirmed by multivariate analysis (P < .005; relative |
| risk = 3.09). On the contrary, no correlation with survival was found in diffuse-type |
| cases according to p53 overexpression. |
| - Many animal behaviors occur in a regular cycle. Two types of cyclic behaviors |
| are circadian rhythms and migration. |
| - source_sentence: Are lactate levels in severe malarial anaemia associated with haemozoin-containing |
| neutrophils and low levels of IL-12? |
| sentences: |
| - Hyperlactataemia is often associated with a poor outcome in severe malaria in |
| African children. To unravel the complex pathophysiology of this condition the |
| relationship between plasma lactate levels, parasite density, pro- and anti-inflammatory |
| cytokines, and haemozoin-containing leucocytes was studied in children with severe |
| falciparum malarial anaemia. Twenty-six children with a primary diagnosis of severe |
| malarial anaemia with any asexual Plasmodium falciparum parasite density and Hb |
| < 5 g/dL were studied and the association of plasma lactate levels and haemozoin-containing |
| leucocytes, parasite density, pro- and anti-inflammatory cytokines was measured. |
| The same associations were measured in non-severe malaria controls (N = 60). Parasite |
| density was associated with lactate levels on admission (r = 0.56, P < 0.005). |
| Moreover, haemozoin-containing neutrophils and IL-12 were strongly associated |
| with plasma lactate levels, independently of parasite density (r = 0.60, P = 0.003 |
| and r = -0.46, P = 0.02, respectively). These associations were not found in controls |
| with uncomplicated malarial anaemia. |
| - one of two female reproductive organs that produces eggs and secretes estrogen. |
| - hydrogen |
| - source_sentence: Does phosphatidylethanol mediate its effects on the vascular endothelial |
| growth factor via HDL receptor in endothelial cells? |
| sentences: |
| - 'Patients having previous bariatric surgery are at risk for weight regain and |
| return of co-morbidities. If an anatomic basis for the failure is identified, |
| many surgeons advocate revision or conversion to a Roux-en-Y gastric bypass. The |
| aim of this study was to determine whether revisional bariatric surgery leads |
| to sufficient weight loss and co-morbidity remission. From 2005-2012, patients |
| undergoing revision were entered into a prospectively maintained database. Perioperative |
| outcomes, including complications, weight loss, and co-morbidity remission, were |
| examined for all patients with a history of a previous vertical banded gastroplasty |
| (VBG) or Roux-en-Y gastric bypass (RYGB). Twenty-two patients with a history of |
| RYGB and 56 with a history of VBG were identified. Following the revisional procedure, |
| the RYGB group experienced 35.8% excess weight loss (%EWL) and a 31.8% morbidity |
| rate. For the VBG group, patients experienced a 46.2% %EWL from their weight before |
| the revisional operation with a 51.8% morbidity rate. Co-morbidity remission rate |
| was excellent. Diabetes (VBG:100%, RYGB: 85.7%), gastroesophageal reflux disease |
| (VBG: 94.4%, RYGB: 80%), and hypertension (VBG: 74.2%, RYGB:60%) demonstrated |
| significant improvement.' |
| - 'Explanation: Let A, B, C represent their respective weights. Then, we have: A |
| + B + C = (45 x 3) = 135 …. (i) A + B = (40 x 2) = 80 …. (ii) B + C = (44 x 2) |
| = 88 ….(iii) Adding (ii) and (iii), we get: A + 2B + C = 168 …. (iv) Subtracting |
| (i) from (iv), we get : B = 33. B’s weight = 33 kg.' |
| - Previous epidemiological studies have shown that light to moderate alcohol consumption |
| has protective effects against coronary heart disease but the mechanisms of the |
| beneficial effect of alcohol are not known. Ethanol may increase high density |
| lipoprotein (HDL) cholesterol concentration, augment the reverse cholesterol transport, |
| or regulate growth factors or adhesion molecules. To study whether qualitative |
| changes in HDL phospholipids mediate part of the beneficial effects of alcohol |
| on atherosclerosis by HDL receptor, we investigated whether phosphatidylethanol |
| (PEth) in HDL particles affects the secretion of vascular endothelial growth factor |
| (VEGF) by a human scavenger receptor CD36 and LIMPII analog-I (CLA-1)-mediated |
| pathway. Human EA.hy 926 endothelial cells were incubated in the presence of native |
| HDL or PEth-HDL. VEGF concentration and CLA-1 protein expression were measured. |
| Human CLA-1 receptor-mediated mechanisms in endothelial cells were studied using |
| CLA-1 blocking antibody and protein kinase inhibitors. Phosphatidylethanol-containing |
| HDL particles caused a 6-fold increase in the expression of CLA-1 in endothelial |
| cells compared with the effect of native HDL. That emergent effect was mediated |
| mainly through protein kinase C and p44/42 mitogen-activated protein kinase pathways. |
| PEth increased the secretion of VEGF and that increase could be abolished by a |
| CLA-1 blocking antibody. |
| - source_sentence: Said to go hand-in-hand with science, what evolves as new materials, |
| designs, and processes are invented? |
| sentences: |
| - Technology evolves as new materials, designs, and processes are invented. |
| - Technological design constraints may be physical or social. |
| - let x=44444444,then 44444445=x+1 88888885=2x-3 44444442=x-2 44444438=x-6 44444444^2=x^2 |
| then substitute it in equation (x+1)(2x-3)(x-2)+(x-6)/x^2 ans is 2x-5 i.e 88888883 |
| pipeline_tag: sentence-similarity |
| library_name: sentence-transformers |
| --- |
| |
| # SentenceTransformer based on BAAI/bge-base-en-v1.5 |
|
|
| This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5). 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:** [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) <!-- at revision a5beb1e3e68b9ab74eb54cfd186867f64f240e1a --> |
| - **Maximum Sequence Length:** 512 tokens |
| - **Output Dimensionality:** 768 dimensions |
| - **Similarity Function:** Cosine Similarity |
| <!-- - **Training Dataset:** Unknown --> |
| <!-- - **Language:** Unknown --> |
| <!-- - **License:** Unknown --> |
|
|
| ### 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': True}) with Transformer model: BertModel |
| (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("danthepol/MNLP_M3_document_encoder") |
| # Run inference |
| sentences = [ |
| 'Said to go hand-in-hand with science, what evolves as new materials, designs, and processes are invented?', |
| 'Technology evolves as new materials, designs, and processes are invented.', |
| 'Technological design constraints may be physical or social.', |
| ] |
| 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] |
| ``` |
|
|
| <!-- |
| ### Direct Usage (Transformers) |
|
|
| <details><summary>Click to see the direct usage in Transformers</summary> |
|
|
| </details> |
| --> |
|
|
| <!-- |
| ### Downstream Usage (Sentence Transformers) |
|
|
| You can finetune this model on your own dataset. |
|
|
| <details><summary>Click to expand</summary> |
|
|
| </details> |
| --> |
|
|
| <!-- |
| ### Out-of-Scope Use |
|
|
| *List how the model may foreseeably be misused and address what users ought not to do with the model.* |
| --> |
|
|
| <!-- |
| ## Bias, Risks and Limitations |
|
|
| *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
| --> |
|
|
| <!-- |
| ### Recommendations |
|
|
| *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
| --> |
|
|
| ## Training Details |
|
|
| ### Training Dataset |
|
|
| #### Unnamed Dataset |
|
|
| * Size: 53,851 training samples |
| * Columns: <code>sentence_0</code> and <code>sentence_1</code> |
| * Approximate statistics based on the first 1000 samples: |
| | | sentence_0 | sentence_1 | |
| |:--------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------| |
| | type | string | string | |
| | details | <ul><li>min: 8 tokens</li><li>mean: 31.16 tokens</li><li>max: 143 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 160.39 tokens</li><li>max: 512 tokens</li></ul> | |
| * Samples: |
| | sentence_0 | sentence_1 | |
| |:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
| | <code>For integers U and V, when U is divided by V, the remainder is odd. Which of the following must be true?</code> | <code>At least one of U and V is odd</code> | |
| | <code>A mailman puts .05% of letters in the wrong mailbox. How many deliveries must he make to misdeliver 2 items?</code> | <code>Let the number of total deliveries be x Then, .05% of x=2 (5/100)*(1/100)*x=2 x=4000</code> | |
| | <code>A certain ball team has an equal number of right- and left-handed players. On a certain day, two-thirds of the players were absent from practice. Of the players at practice that day, two-third were left handed. What is the ratio of the number of right-handed players who were not at practice that day to the number of lefthanded players who were not at practice?</code> | <code>Say the total number of players is 18, 9 right-handed and 9 left-handed. On a certain day, two-thirds of the players were absent from practice --> 12 absent and 6 present. Of the players at practice that day, one-third were left-handed --> 6*2/3=4 were left-handed and 2 right-handed. The number of right-handed players who were not at practice that day is 9-2=7. The number of left-handed players who were not at practice that days is 9-4=5. The ratio = 7/5.</code> | |
| * Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: |
| ```json |
| { |
| "scale": 20.0, |
| "similarity_fct": "cos_sim" |
| } |
| ``` |
|
|
| ### Training Hyperparameters |
| #### Non-Default Hyperparameters |
|
|
| - `per_device_train_batch_size`: 32 |
| - `per_device_eval_batch_size`: 32 |
| - `multi_dataset_batch_sampler`: round_robin |
| |
| #### All Hyperparameters |
| <details><summary>Click to expand</summary> |
| |
| - `overwrite_output_dir`: False |
| - `do_predict`: False |
| - `eval_strategy`: no |
| - `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`: 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`: 3 |
| - `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 |
| |
| </details> |
| |
| ### Training Logs |
| | Epoch | Step | Training Loss | |
| |:------:|:----:|:-------------:| |
| | 0.2971 | 500 | 0.1286 | |
| | 0.5942 | 1000 | 0.0769 | |
| | 0.8913 | 1500 | 0.0682 | |
| | 1.1884 | 2000 | 0.0416 | |
| | 1.4854 | 2500 | 0.0369 | |
| | 1.7825 | 3000 | 0.0326 | |
| | 2.0796 | 3500 | 0.0331 | |
| | 2.3767 | 4000 | 0.0213 | |
| | 2.6738 | 4500 | 0.0211 | |
| | 2.9709 | 5000 | 0.0207 | |
| |
| |
| ### 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: 3.2.0 |
| - 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", |
| } |
| ``` |
| |
| #### 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} |
| } |
| ``` |
| |
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| ## Model Card Authors |
| |
| *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.* |
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