---
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:218496
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: "when dividing involving a multiple of 10, gives an answer 10 times\
\ bigger than it should be\n\ndivide decimals by 10(multiplying and dividing with\
\ decimals).\nquestion: 43.2 \\div 10= \ncorrect answer: 4.32 \nincorrect answer:\
\ 33.2"
sentences:
- Does not recognise that a shape translated would not change orientation
- Thinks you can find missing values in a given table by treating the row as linear
and adding on the difference between the first two values given.
- Subtracts instead of divides
- source_sentence: "incorrectly cancels what they believe is a factor in algebraic\
\ fractions\n\nsimplify an algebraic fraction by factorising the numerator(simplifying\
\ algebraic fractions).\nquestion: simplify the following, if possible: \\frac{m^{2}+2\
\ m-3}{m-3} \ncorrect answer: does not simplify\nincorrect answer: m+1"
sentences:
- Does not know units of area should be squared
- Thinks all lines on a net will form edges in 3D
- 'Does not know that to factorise a quadratic expression, to find two numbers that
add to give the coefficient of the x term, and multiply to give the non variable
term
'
- source_sentence: "believes that the order of operations does not affect the answer\
\ to a calculation\n\nuse the order of operations to carry out calculations involving\
\ powers(bidmas).\nquestion: \\[\n3 \\times 2+4-5\n\\]\nwhere do the brackets\
\ need to go to make the answer equal 13 ?\ncorrect answer: 3 \\times(2+4)-5 \n\
incorrect answer: does not need brackets"
sentences:
- Thinks that when you cancel identical terms from the numerator and denominator,
they just disappear
- Believes both the x and y co-ordinates of the x-intercept of a quadratic are derived
from the constants in the factorised form.
- 'Confuses the order of operations, believes addition comes before multiplication '
- source_sentence: "believes that the order of operations does not affect the answer\
\ to a calculation\n\nuse the order of operations to carry out calculations involving\
\ powers(bidmas).\nquestion: \\[\n3 \\times 2+4-5\n\\]\nwhere do the brackets\
\ need to go to make the answer equal 13 ?\ncorrect answer: 3 \\times(2+4)-5 \n\
incorrect answer: does not need brackets"
sentences:
- 'Confuses the order of operations, believes addition comes before multiplication '
- Does not recognise the properties of a kite
- 'Confuses the order of operations, believes addition comes before multiplication '
- source_sentence: "believes percentages cannot be converted into fractions\n\nconvert\
\ two digit integer percentages to fractions(converting between fractions and\
\ percentages).\nquestion: convert this percentage to a fraction\n 62 \\% \ncorrect\
\ answer: \\frac{31}{50} \nincorrect answer: none of these"
sentences:
- Believes the gradients of perpendicular lines are reciprocals of the same sign
- Does not know the properties of a rectangle
- Does not understand a percentage is out of 100
---
# SentenceTransformer
This is a [sentence-transformers](https://www.SBERT.net) model trained. 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
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 1024 tokens
- **Similarity Function:** Cosine Similarity
### 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': 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})
)
```
## 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("Gurveer05/gte-base-eedi-2024")
# Run inference
sentences = [
'believes percentages cannot be converted into fractions\n\nconvert two digit integer percentages to fractions(converting between fractions and percentages).\nquestion: convert this percentage to a fraction\n 62 \\% \ncorrect answer: \\frac{31}{50} \nincorrect answer: none of these',
'Does not understand a percentage is out of 100',
'Believes the gradients of perpendicular lines are reciprocals of the same sign',
]
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]
```
## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 218,496 training samples
* Columns: FullText, GroundTruthMisconception, and PredictMisconception
* Approximate statistics based on the first 1000 samples:
| | FullText | GroundTruthMisconception | PredictMisconception |
|:--------|:------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | string | string | string |
| details | - min: 58 tokens
- mean: 95.45 tokens
- max: 159 tokens
| - min: 8 tokens
- mean: 17.0 tokens
- max: 38 tokens
| - min: 6 tokens
- mean: 15.27 tokens
- max: 40 tokens
|
* Samples:
| FullText | GroundTruthMisconception | PredictMisconception |
|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------|
| believes that the order of operations does not affect the answer to a calculation
use the order of operations to carry out calculations involving powers(bidmas).
question: \[
3 \times 2+4-5
\]
where do the brackets need to go to make the answer equal 13 ?
correct answer: 3 \times(2+4)-5
incorrect answer: does not need brackets | Confuses the order of operations, believes addition comes before multiplication | Believes infinite gradient is not possible in real life. |
| believes that the order of operations does not affect the answer to a calculation
use the order of operations to carry out calculations involving powers(bidmas).
question: \[
3 \times 2+4-5
\]
where do the brackets need to go to make the answer equal 13 ?
correct answer: 3 \times(2+4)-5
incorrect answer: does not need brackets | Confuses the order of operations, believes addition comes before multiplication | Struggles to draw 3D shapes on isometric paper |
| believes that the order of operations does not affect the answer to a calculation
use the order of operations to carry out calculations involving powers(bidmas).
question: \[
3 \times 2+4-5
\]
where do the brackets need to go to make the answer equal 13 ?
correct answer: 3 \times(2+4)-5
incorrect answer: does not need brackets | Confuses the order of operations, believes addition comes before multiplication | Believes an upward slope on a distance-time graph means travelling back towards the starting point. |
* Loss: [MultipleNegativesRankingLoss](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
- `gradient_accumulation_steps`: 16
- `eval_accumulation_steps`: 16
- `learning_rate`: 2e-05
- `weight_decay`: 0.01
- `num_train_epochs`: 2
- `lr_scheduler_type`: cosine_with_restarts
- `warmup_ratio`: 0.1
- `fp16`: True
- `batch_sampler`: no_duplicates
#### All Hyperparameters
Click to expand
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: no
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 8
- `per_device_eval_batch_size`: 8
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 16
- `eval_accumulation_steps`: 16
- `learning_rate`: 2e-05
- `weight_decay`: 0.01
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 2
- `max_steps`: -1
- `lr_scheduler_type`: cosine_with_restarts
- `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`: False
- `fp16`: True
- `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}
- `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`: False
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `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
- `dispatch_batches`: None
- `split_batches`: 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
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional