---
base_model: sentence-transformers/paraphrase-mpnet-base-v2
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:2940
- loss:MultipleNegativesSymmetricRankingLoss
widget:
- source_sentence: 'Question:
Assign probabilities to single events using fractions. There are 4 red balls
and 3 yellow balls in a ∪. The image shows the ∪ with the 4 red balls and 3
yellow balls. What is the probability of picking a red ball?.
Answer: (4 / 3)'
sentences:
- Does not recognise difference of two squares
- Gives the probability out of the number of non-successes rather than the total
- Shares equally instead of in given ratio
- source_sentence: 'Question:
Convert from hours to minutes. 3 hours is the same as ___________ minutes.
Answer: 30'
sentences:
- Starts at the wrong end of the dividend when dividing
- Mixes up squaring and multiplying by 2 or doubling
- Thinks there are 10 minutes in an hour
- source_sentence: 'Question:
Divide integers up to 2 digits using long division. When using long-division to
solve this problem, what digit should replace the triangle to give the correct
answer (quotient)? Division sum written out in Bus Stop format with dividend =
92, divisor = 4, the digits of the quotient are represented by a yellow star and
a purple triangle..
Answer: 0'
sentences:
- Believes multiplying a positive by a negative gives a positive answer
- Confuses linear and reciprocal graphs
- Starts at the wrong end of the dividend when dividing
- source_sentence: 'Question:
Know that the factorised form of a quadratic is useful for finding where the curve
crosses the x-axis. y=(x-3)(x-5) Where does this curve intercept the x axis?.
Answer: (3,5) and (-3,-5)'
sentences:
- Calculates area when asked for perimeter
- Believes both the x and y co-ordinates of the x-intercept of a quadratic are derived
from the constants in the factorised form.
- Does not convert measurements to have the same units before calculating area or
volume
- source_sentence: 'Question:
Understand key loci terms like equidistant and perpendicular. A set of axes: x-axis
from -4 to 4, y-axis from -4 to 4. A red line is drawn from (-2,2) to (2,2). The
red line is _________ to the y axis..
Answer: Equidistant'
sentences:
- Does not know the definition of a rational number
- Believes squaring a negative number just changes the sign
- Does not know the meaning of perpendicular
---
# SentenceTransformer based on sentence-transformers/paraphrase-mpnet-base-v2
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2) on the csv dataset. 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:** [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2)
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 tokens
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
- csv
### 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': False}) with Transformer model: MPNetModel
(1): Pooling({'word_embedding_dimension': 768, '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})
)
```
## 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/paraphrase-mpnet-eedi-2024")
# Run inference
sentences = [
'Question:\nUnderstand key loci terms like equidistant and perpendicular. A set of axes: x-axis from -4 to 4, y-axis from -4 to 4. A red line is drawn from (-2,2) to (2,2). The red line is _________ to the y axis..\nAnswer: Equidistant',
'Does not know the meaning of perpendicular',
'Believes squaring a negative number just changes the sign',
]
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]
```
## Training Details
### Training Dataset
#### csv
* Dataset: csv
* Size: 2,940 training samples
* Columns: sentence1 and sentence2
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 |
|:--------|:------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | string | string |
| details | - min: 20 tokens
- mean: 61.48 tokens
- max: 312 tokens
| - min: 4 tokens
- mean: 14.98 tokens
- max: 38 tokens
|
* Samples:
| sentence1 | sentence2 |
|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------|
| Question:
Find missing angles in a scalene triangle. What is the size of angle p ? A triangle with angles labelled 49 degrees, 51 degrees and p [not to scale].
Answer: Not enough information | Does not know that angles in a triangle sum to 180 degrees |
| Question:
Solve quadratic equations using balancing. A student wishes to solve the equation below.
Which of the following is a correct next step?
(
(d+3)^2-25=0
)
Step 1
(
(d+3)^2=25
).
Answer: d+3=12.5 | Believes the inverse of square rooting is halving |
| Question:
Find missing angles using angles around a point. What is the size of angle x ? Angles around a point split into two parts, one is labelled 290 degrees and the other x.
Answer: 45° | Does not know that angles around a point sum to 360 |
* Loss: [MultipleNegativesSymmetricRankingLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativessymmetricrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim"
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 32
- `num_train_epochs`: 20
- `fp16`: True
- `load_best_model_at_end`: True
- `batch_sampler`: no_duplicates
#### All Hyperparameters
Click to expand
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `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`: 1.0
- `num_train_epochs`: 20
- `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`: 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`: 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}
- `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
- `eval_use_gather_object`: False
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional