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bge-large-eedi-2024

Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:2442
loss:MultipleNegativesRankingLoss
text-embeddings-inference
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 ---
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:2940
- loss:MultipleNegativesSymmetricRankingLoss
widget:
- source_sentence: Enlarge a shape, with a centre of enlargement given, by a positive
scale factor bigger than 1, where the centre of enlargement lies on the edge or
outside of the object The triangle is enlarged by scale factor 3, with the centre
of enlargement at (1,0). What are the new coordinates of the point marked T ?
![A coordinate grid with the x-axis going from -1 to 10 and the y-axis going from
-1 to 7. 3 points are plotted and joined with straight lines to form a triangle.
The points are (1,1), (1,4) and (3,1). Point (3,1) is labelled as T. Point (1,0)
is also plotted.]() (9,3)
sentences:
- Confuses powers and multiples
- Enlarges by the wrong centre of enlargement
- When asked for factors of an algebraic expression, thinks any part of a term will
be a factor
- source_sentence: 'Identify a right-angled triangle from a description of the properties
A triangle has the following angles: 90^, 45^, 45^ Statement 1. It must be a right
angled triangle Statement 2. It must be an isosceles triangle Which is true? Statement
1'
sentences:
- When solving a problem using written division (bus-stop method), does the calculation
from right to left
- Thinks finding a fraction of an amount means subtracting from that amount
- Believes isosceles triangles cannot have right angles
- source_sentence: Convert from kilometers to miles 1 km≈ 0.6 miles 4 km≈□ miles 0.24
sentences:
- Believes multiplying two negatives gives a negative answer
- Believes two lines of the same length are parallel
- When multiplying decimals, ignores place value and just multiplies the digits
- source_sentence: Identify the order of rotational symmetry of a shape Which shape
has rotational symmetry order 4 ? ![Trapezium]()
sentences:
- Believes the whole and remainder are the other way when changing an improper fraction
to a mixed number
- Does not know how to find order of rotational symmetry
- Fails to reflect across mirror line
- source_sentence: Identify whether two shapes are similar or not Tom and Katie are
discussing similarity. Who is correct? Tom says these two rectangles are similar
![Two rectangles of different sizes. One rectangle has width 2cm and height 3cm.
The other rectangle has width 4cm and height 9cm. ]() Katie says these two rectangles
are similar ![Two rectangles of different sizes. One rectangle has width 4cm and
height 6cm. The other rectangle has width 7cm and height 9cm. ]() Only Katie
sentences:
- Does not recognise when one part of a fraction is the negative of the other
- When solving simultaneous equations, thinks they can't multiply each equation
by a different number
- Thinks adding the same value to each side makes shapes similar
---
# 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) on the csv dataset. 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) <!-- at revision d4aa6901d3a41ba39fb536a557fa166f842b0e09 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 1024 tokens
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
- csv
<!-- - **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': 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("Gurveer05/bge-large-eedi-2024")
# Run inference
sentences = [
'Identify whether two shapes are similar or not Tom and Katie are discussing similarity. Who is correct? Tom says these two rectangles are similar ![Two rectangles of different sizes. One rectangle has width 2cm and height 3cm. The other rectangle has width 4cm and height 9cm. ]() Katie says these two rectangles are similar ![Two rectangles of different sizes. One rectangle has width 4cm and height 6cm. The other rectangle has width 7cm and height 9cm. ]() Only Katie',
'Thinks adding the same value to each side makes shapes similar',
"When solving simultaneous equations, thinks they can't multiply each equation by a different number",
]
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]
```
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## Training Details
### Training Dataset
#### csv
* Dataset: csv
* Size: 2,940 training samples
* Columns: <code>sentence1</code> and <code>sentence2</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 |
|:--------|:------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 13 tokens</li><li>mean: 56.03 tokens</li><li>max: 249 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 15.19 tokens</li><li>max: 39 tokens</li></ul> |
* Samples:
| sentence1 | sentence2 |
|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------|
| <code>Read a fraction on a scale where the required number is marked by a dash between two numbers What fraction is the arrow pointing to? ![An image of a numberline with 5 dashes. On the leftmost dash is the number 1/6. On the rightmost dash is the number 3/6. An arrow points to the 4th dash from the left]() 3/4</code> | <code>When reading a dash on a number line does not take into account the number at the start or the width of each division</code> |
| <code>Substitute positive non-integer values into expressions involving powers or roots Jo and Paul are discussing quadratic equations. Jo says there is no value of x that can make (1-x)^2 negative. Paul says there is no value of x that can make 1-x^2 positive. Who is correct? Both Jo and Paul</code> | <code>Assumes a fact without considering enough examples</code> |
| <code>Recognise and use efficient methods for mental multiplication Tom and Katie are discussing mental multiplication strategies. Tom says 15 × 42=154 × 2 Katie says 15 × 42=(15 × 4)+(15 × 2) Who is correct? Only Tom</code> | <code>Does not correctly apply the commutative property of multiplication</code> |
* Loss: [<code>MultipleNegativesSymmetricRankingLoss</code>](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`: 16
- `per_device_eval_batch_size`: 16
- `num_train_epochs`: 20
- `fp16`: True
- `load_best_model_at_end`: True
- `batch_sampler`: no_duplicates
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `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
</details>
### Training Logs
| Epoch | Step | Training Loss |
|:-------:|:-------:|:-------------:|
| 0.25 | 23 | 1.0714 |
| 0.5 | 46 | 0.9487 |
| 0.75 | 69 | 0.8001 |
| 1.0 | 92 | 0.7443 |
| 1.25 | 115 | 0.3951 |
| 1.5 | 138 | 0.3903 |
| 1.75 | 161 | 0.3867 |
| 2.0 | 184 | 0.3386 |
| 2.25 | 207 | 0.2206 |
| 2.5 | 230 | 0.2051 |
| 2.75 | 253 | 0.2098 |
| 3.0 | 276 | 0.1989 |
| 3.25 | 299 | 0.1486 |
| 3.5 | 322 | 0.1463 |
| 3.75 | 345 | 0.1453 |
| 4.0 | 368 | 0.1237 |
| 4.25 | 391 | 0.0956 |
| 4.5 | 414 | 0.0939 |
| 4.75 | 437 | 0.1115 |
| 5.0 | 460 | 0.0925 |
| 5.25 | 483 | 0.0778 |
| 5.5 | 506 | 0.0744 |
| 5.75 | 529 | 0.09 |
| 6.0 | 552 | 0.0782 |
| 6.25 | 575 | 0.0454 |
| 6.5 | 598 | 0.0697 |
| 6.75 | 621 | 0.059 |
| 7.0 | 644 | 0.033 |
| 7.25 | 667 | 0.0309 |
| 7.5 | 690 | 0.0548 |
| 7.75 | 713 | 0.0605 |
| **8.0** | **736** | **0.0431** |
| 8.25 | 759 | 0.0224 |
| 8.5 | 782 | 0.0381 |
| 8.75 | 805 | 0.0451 |
| 9.0 | 828 | 0.0169 |
| 9.25 | 851 | 0.0228 |
| 9.5 | 874 | 0.0257 |
* The bold row denotes the saved checkpoint.
### Framework Versions
- Python: 3.10.14
- Sentence Transformers: 3.1.0
- Transformers: 4.44.0
- PyTorch: 2.4.0
- Accelerate: 0.33.0
- Datasets: 2.19.2
- Tokenizers: 0.19.1
## 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",
}
```
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