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()
)pip install -U sentence-transformers1from sentence_transformers import SentenceTransformer
2
3# Download from the 🤗 Hub
4model = SentenceTransformer("minsuas/Misconceptions_Fisrt")
5# Run inference
6sentences = [
7 'Subject: Quadratic Equations\nConstruct: Solve quadratic equations using factorisation in the form x(x + b)\nQuestion: Solve this equation, giving all solutions:\n\\[\nk^{2}=4 k\n\\]\nIncorrect Answer: \\( k=4 \\)',
8 "Believes they can divide by a variable without checking whether it could equal zero\nThe misconception described here pertains to the process of solving algebraic equations, particularly when dividing both sides of an equation by a variable. The misconception is that one can divide by a variable without considering whether that variable could potentially be zero.\n\nIn algebra, dividing both sides of an equation by a variable (let's say \\(x\\)) is generally valid only if \\(x \\neq 0\\). If \\(x\\) could be zero, then dividing by \\(x\\) is not allowed because division by zero is undefined in mathematics. This oversight can lead to losing a solution (specifically, \\(x = 0\\)) or deriving incorrect conclusions.\n\nFor example, consider the equation \\(x^2 = 3x\\). If one incorrectly divides both sides by \\(x\\) without checking whether \\(x\\) can be zero, they might reduce it to \\(x = 3\\), thereby missing the solution \\(x = 0\\).\n\nThe correct approach would be to rearrange the equation to \\(x^2 - 3x = 0\\), factor it to \\(x(x - 3) = 0\\), and then conclude that \\(x = 0\\) or \\(x = 3\\), thus ensuring no solutions are lost.",
9 'When solving an equation, multiplies instead of dividing\nThe passage is highlighting a common mistake made when solving mathematical equations, where a student might mistakenly multiply when they should be dividing. This can happen in various contexts, such as solving for a variable in an equation or converting units. For example, if a problem requires you to divide both sides of an equation by a number to isolate the variable, mistakenly multiplying instead would lead to an incorrect solution. \n\nThe key here is to carefully read the problem, understand the operations needed, and apply the correct mathematical operations to solve the equation accurately.',
10]
11embeddings = model.encode(sentences)
12print(embeddings.shape)
13# [3, 384]
14
15# Get the similarity scores for the embeddings
16similarities = model.similarity(embeddings, embeddings)
17print(similarities.shape)
18# [3, 3]anchor, positive, and negative| anchor | positive | negative | |
|---|---|---|---|
| type | string | string | string |
| details |
|
|
|
| anchor | positive | negative |
|---|---|---|
Subject: Cubics and Reciprocals[object Object]Construct: Given a positive x value, find the corresponding y value for reciprocal graphs[object Object]Question: This is a part of the table of values for the equation ( y=\frac{3}{x} ) \begin{tabular}{ | l | l |
Subject: Angle Facts with Parallel Lines[object Object]Construct: Identify a transversal[object Object]Question: What is the name given to the red line that intersects the two dashed lines? [object Object][object Object]Incorrect Answer: Parallel | Does not know the meaning of the word parallel[object Object]The passage is indicating a misconception related to a math problem, specifically one that involves the concept of "parallel." In mathematics, particularly in geometry, "parallel" refers to lines or planes that are equidistant from each other at every point and never intersect, no matter how far they are extended. A misunderstanding or lack of knowledge about this definition can lead to errors when solving problems that involve parallel lines or planes, such as determining angles or distances. Thus, to correctly interpret and solve problems involving parallel lines or planes, one must understand that they maintain a constant distance from each other and never meet. | Believes parallel is the term used to describe two lines at right angles[object Object]The passage is discussing a common misconception about the term "parallel." The misconception is that some people believe "parallel" refers to two lines that are at right angles (perpendicular) to each other. However, this is incorrect. [object Object][object Object]In fact, "parallel" refers to two lines in a plane that are always the same distance apart and never meet, no matter how far they are extended. In contrast, two lines are considered perpendicular if they meet at right angles (90 degrees). Therefore, the misconception confuses the definitions of parallel and perpendicular lines. |
Subject: Sharing in a Ratio[object Object]Construct: Divide a quantity into two parts for a given a ratio, where each part is an integer[object Object]Question: Share ( £360 ) in the ratio ( 2: 7 )[object Object]Incorrect Answer: ( £ 180: £ 51 ) | Divides total amount by each side of the ratio instead of dividing by the sum of the parts[object Object]The misconception described refers to a mistake made when dividing a total amount according to a given ratio. For instance, if someone has to divide $100 in the ratio 2:3, a correct approach would be to first add the parts of the ratio (2+3=5) to find the total number of parts. Then, divide the total amount by this sum ($100 ÷ 5 = $20) to determine the value of one part. This $20 can then be multiplied by each number in the ratio (2 and 3) to correctly distribute the $100.[object Object][object Object]The misconception occurs when someone divides the total amount ($100) by each individual number in the ratio (2 and 3) rather than by the sum of the parts (5). This method would incorrectly distribute the $100, as it does not account for the proportional relationship that the ratio is meant to establish. | Estimates shares of a ratio instead of calculating[object Object]The passage is discussing a common mistake made in mathematics, particularly when dealing with ratio problems. The misconception lies in estimating the shares or parts of a ratio rather than calculating them accurately. For example, if a problem involves dividing a quantity in the ratio of 2:3, the misconception would be to guess or estimate what parts of the quantity correspond to 2 and 3, instead of using the correct method to find the exact shares. The correct approach involves first adding the parts of the ratio (in this case, 2 + 3 = 5) and then using this sum to calculate each part's exact share of the total quantity. Thus, it's important to calculate each part of the ratio precisely rather than estimating. |
CachedMultipleNegativesRankingLoss with these parameters:
1{
2 "scale": 20.0,
3 "similarity_fct": "cos_sim"
4}per_device_train_batch_size: 512num_train_epochs: 1lr_scheduler_type: cosinewarmup_ratio: 0.1fp16: Trueoverwrite_output_dir: Falsedo_predict: Falseeval_strategy: noprediction_loss_only: Trueper_device_train_batch_size: 512per_device_eval_batch_size: 8per_gpu_train_batch_size: Noneper_gpu_eval_batch_size: Nonegradient_accumulation_steps: 1eval_accumulation_steps: Nonetorch_empty_cache_steps: Nonelearning_rate: 5e-05weight_decay: 0.0adam_beta1: 0.9adam_beta2: 0.999adam_epsilon: 1e-08max_grad_norm: 1.0num_train_epochs: 1max_steps: -1lr_scheduler_type: cosinelr_scheduler_kwargs: {}warmup_ratio: 0.1warmup_steps: 0log_level: passivelog_level_replica: warninglog_on_each_node: Truelogging_nan_inf_filter: Truesave_safetensors: Truesave_on_each_node: Falsesave_only_model: Falserestore_callback_states_from_checkpoint: Falseno_cuda: Falseuse_cpu: Falseuse_mps_device: Falseseed: 42data_seed: Nonejit_mode_eval: Falseuse_ipex: Falsebf16: Falsefp16: Truefp16_opt_level: O1half_precision_backend: autobf16_full_eval: Falsefp16_full_eval: Falsetf32: Nonelocal_rank: 0ddp_backend: Nonetpu_num_cores: Nonetpu_metrics_debug: Falsedebug: []dataloader_drop_last: Falsedataloader_num_workers: 0dataloader_prefetch_factor: Nonepast_index: -1disable_tqdm: Falseremove_unused_columns: Truelabel_names: Noneload_best_model_at_end: Falseignore_data_skip: Falsefsdp: []fsdp_min_num_params: 0fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}fsdp_transformer_layer_cls_to_wrap: Noneaccelerator_config: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}deepspeed: Nonelabel_smoothing_factor: 0.0optim: adamw_torchoptim_args: Noneadafactor: Falsegroup_by_length: Falselength_column_name: lengthddp_find_unused_parameters: Noneddp_bucket_cap_mb: Noneddp_broadcast_buffers: Falsedataloader_pin_memory: Truedataloader_persistent_workers: Falseskip_memory_metrics: Trueuse_legacy_prediction_loop: Falsepush_to_hub: Falseresume_from_checkpoint: Nonehub_model_id: Nonehub_strategy: every_savehub_private_repo: Nonehub_always_push: Falsegradient_checkpointing: Falsegradient_checkpointing_kwargs: Noneinclude_inputs_for_metrics: Falseinclude_for_metrics: []eval_do_concat_batches: Truefp16_backend: autopush_to_hub_model_id: Nonepush_to_hub_organization: Nonemp_parameters:auto_find_batch_size: Falsefull_determinism: Falsetorchdynamo: Noneray_scope: lastddp_timeout: 1800torch_compile: Falsetorch_compile_backend: Nonetorch_compile_mode: Nonedispatch_batches: Nonesplit_batches: Noneinclude_tokens_per_second: Falseinclude_num_input_tokens_seen: Falseneftune_noise_alpha: Noneoptim_target_modules: Nonebatch_eval_metrics: Falseeval_on_start: Falseuse_liger_kernel: Falseeval_use_gather_object: Falseaverage_tokens_across_devices: Falseprompts: Nonebatch_sampler: batch_samplermulti_dataset_batch_sampler: proportional1@inproceedings{reimers-2019-sentence-bert,
2 title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
3 author = "Reimers, Nils and Gurevych, Iryna",
4 booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
5 month = "11",
6 year = "2019",
7 publisher = "Association for Computational Linguistics",
8 url = "https://arxiv.org/abs/1908.10084",
9}1@misc{gao2021scaling,
2 title={Scaling Deep Contrastive Learning Batch Size under Memory Limited Setup},
3 author={Luyu Gao and Yunyi Zhang and Jiawei Han and Jamie Callan},
4 year={2021},
5 eprint={2101.06983},
6 archivePrefix={arXiv},
7 primaryClass={cs.LG}
8}