---
base_model: BAAI/bge-large-en-v1.5
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:4370
- loss:CosineSimilarityLoss
widget:
- source_sentence: '
Construct: Recognise a linear graph from its shape
Subject: Finding the Gradient and Intercept of a Line from the Equation
Question: Use a graphing program (e.g. Desmos) to plot the following pairs of
functions.
\[
y=3 \text { and } y=-2
\]
Tom says both functions are linear
Katie says both functions are vertical lines
Who is correct?
Incorrect Answer: Neither is correct
Correct Answer: Only
Tom
'
sentences:
- Believes the coefficent of x in an expanded quadratic comes from multiplying the
two numbers in the brackets
- Does not know the properties of a linear graph
- Misremembers the quadratic formula
- source_sentence: '
Construct: Multiply two decimals together with the same number of decimal places
Subject: Multiplying and Dividing with Decimals
Question: \( 0.6 \times 0.4= \)
Incorrect Answer: \( 2.4 \)
Correct Answer: \( 0.24 \)
'
sentences:
- When asked to solve simultaneous equations, believes they can just find values
that work in one equation
- Believes the solutions of a quadratic equation are the constants in the factorised
form
- When multiplying decimals, divides by the wrong power of 10 when reinserting the
decimal
- source_sentence: '
Construct: Estimate the volume or capacity of an object
Subject: Volume of Prisms
Question: Each of these measurements matches one of these objects. ![An image
of 4 objects and 4 measurements. The objects are an egg cup, a cereal box, a chest
of drawers and a piggy bank. And, the measurements are 87 cm^3, 1013 cm^3, 4172
cm^3 and 197,177 cm^3.]() Which measurement most likely matches the egg cup?
Incorrect Answer: \( 197177 \mathrm{~cm}^{3} \)
Correct Answer: \( 87 \mathrm{~cm}^{3} \)
'
sentences:
- Confuses quadratic and exponential graphs
- Cannot estimate the relative volume order, for different objects
- Does not know how many days are in a leap year
- source_sentence: '
Construct: Carry out division problems involving one negative integer
Subject: Multiplying and Dividing Negative Numbers
Question: \( 12 \div(-4)= \)
Incorrect Answer: \( 3 \)
Correct Answer: \( -3 \)
'
sentences:
- Believes dividing a positive by a negative gives a positive answer
- Believes -a is always smaller than a, ignoring the possibility that a is negative
- Subtracts instead of divides
- source_sentence: '
Construct: Construct frequency tables
Subject: Frequency tables
Question: Dave has recorded the number of pets his classmates have in the frequency
table on the right. \begin{tabular}{|c|c|}
\hline Number of pets & Frequency \\
\hline \( 0 \) & \( 4 \) \\
\hline \( 1 \) & \( 6 \) \\
\hline \( 2 \) & \( 3 \) \\
\hline \( 3 \) & \( 2 \) \\
\hline \( 4 \) & \( 5 \) \\
\hline
\end{tabular} If Dave wanted to work out the total number of pets own by his classmates,
what would be a useful column to include?
Incorrect Answer: Number of pets -
Frequency
Correct Answer: Number of pets \( x \) Frequency
'
sentences:
- Subtracts rather than multiplies when calculating total frequency
- Does not follow the arrows through a function machine, changes the order of the
operations asked.
- 'Believes the intersection in a prime factor venn diagram does not contribute
to the size of the number represented by a circle '
---
# SentenceTransformer based on BAAI/bge-large-en-v1.5
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5). 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
- **Base model:** [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5)
- **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})
(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("VaggP/bge-fine-tuned")
# Run inference
sentences = [
'\nConstruct: Construct frequency tables\nSubject: Frequency tables\nQuestion: Dave has recorded the number of pets his classmates have in the frequency table on the right. \\begin{tabular}{|c|c|}\n\\hline Number of pets & Frequency \\\\\n\\hline \\( 0 \\) & \\( 4 \\) \\\\\n\\hline \\( 1 \\) & \\( 6 \\) \\\\\n\\hline \\( 2 \\) & \\( 3 \\) \\\\\n\\hline \\( 3 \\) & \\( 2 \\) \\\\\n\\hline \\( 4 \\) & \\( 5 \\) \\\\\n\\hline\n\\end{tabular} If Dave wanted to work out the total number of pets own by his classmates, what would be a useful column to include?\nIncorrect Answer: Number of pets -\nFrequency\nCorrect Answer: Number of pets \\( x \\) Frequency\n',
'Subtracts rather than multiplies when calculating total frequency',
'Does not follow the arrows through a function machine, changes the order of the operations asked.',
]
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: 4,370 training samples
* Columns: sentence_0, sentence_1, and label
* Approximate statistics based on the first 1000 samples:
| | sentence_0 | sentence_1 | label |
|:--------|:------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:--------------------------------------------------------------|
| type | string | string | float |
| details | - min: 38 tokens
- mean: 98.75 tokens
- max: 414 tokens
| - min: 4 tokens
- mean: 14.91 tokens
- max: 38 tokens
| - min: 1.0
- mean: 1.0
- max: 1.0
|
* Samples:
| sentence_0 | sentence_1 | label |
|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------|:-----------------|
|
Construct: Construct a pictogram involving fractions of symbols
Subject: Pictogram
Question: This pictogram shows the different types of music Bob has in his music collection.
Bob has \( 2 \) rave CDs.
How would he display this on the pictogram? ![A pictogram showing the number of CDs Bob has in his musical collection. Pop has 3 and a half symbols, rock has 2 symbols, blues has 2 and a quarter symbols, jazz has 3 and a quarter symbols and classical has 1 and three-quarter symbols. Each symbol represents 4 CDs.]()
Incorrect Answer: ![\( 00 \)]()
Correct Answer: ![\( 0 \)]()
When interpreting a pictogram, thinks each symbol stands for 1 | 1.0 |
|
Construct: Use brackets to write function machines as calculations
Subject: Writing Expressions
Question: Tom and Katie are arguing about the result of this Function Machine:
Tom says the output is: \( 3 n-12 \)
Katie says the output is: \( 3(n-4) \)
Who is correct? ![A function machine with input n and operations subtract 4, multiply by 3]()
Incorrect Answer: Only Tom
Correct Answer: Both Tom and Katie
Does not think a factorised expression is equivalent to its multiplied out form | 1.0 |
|
Construct: Interpret linear sections of real life graphs
Subject: Real Life Graphs
Question: The graph on the right shows the mass of sand in a bucket over time
What might the horizontal section represent? ![A graph with time (secs) on the horizontal axis and mass (g) on the vertical axis. The graph starts at the origin, travels in a straight line up and right, travels horizontally, then travels in a straight line down and right back to the x-axis, more steeply than the start. ]()
Incorrect Answer: Sand is being tipped out
Correct Answer: The bucket is full
Believes a horizontal line can show a constant rate of change | 1.0 |
* Loss: [CosineSimilarityLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
```json
{
"loss_fct": "torch.nn.modules.loss.MSELoss"
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `num_train_epochs`: 1
- `multi_dataset_batch_sampler`: round_robin
#### 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`: 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`: 1
- `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}
- `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
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: round_robin