File size: 22,114 Bytes
e137031 50bb180 c70df6e e137031 caf33fd e137031 caf33fd e137031 caf33fd e137031 caf33fd e137031 caf33fd e137031 caf33fd e137031 caf33fd e137031 50bb180 caf33fd e137031 caf33fd e137031 caf33fd c70df6e caf33fd c70df6e caf33fd c70df6e caf33fd c70df6e e137031 caf33fd c70df6e e137031 c70df6e 50bb180 e137031 c70df6e e137031 caf33fd e137031 caf33fd e137031 c70df6e e137031 c70df6e e137031 50bb180 e137031 c70df6e e137031 c70df6e caf33fd e137031 c70df6e 50bb180 c70df6e e137031 c70df6e e137031 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 |
---
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:2442
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: 'Construct: Identify the order of rotational symmetry of a shape.
Question: Which shape has the lowest order of rotational symmetry?
Options:
A. Equilateral triangle
B. Circle
C. Square
D. Trapezium
Correct Answer: Trapezium
Incorrect Answer: Circle'
sentences:
- When multiplying decimals, divides by the wrong power of 10 when reinserting the
decimal
- Does not understand inequality notation
- Does not know how to find order of rotational symmetry
- source_sentence: "Construct: Solve three or more step linear equations, with the\
\ variable on one side, involving negative integers.\n\nQuestion: Tom and Katie\
\ are discussing how to solve:\n(5 x / 3)-1=-2\n\nTom says a correct next line\
\ of working could be: 5 x-3=-6 \n\nKatie says a correct next line of working\
\ could be: (5 x / 3)=-3 \n\nWho is correct?\n\nOptions:\nA. Only Tom\nB. Only\
\ Katie\nC. Both Tom and Katie\nD. Neither is correct\n\nCorrect Answer: Only\
\ Tom\n\nIncorrect Answer: Neither is correct"
sentences:
- Mixes up the value of two terms when substituting
- Thinks there are 100 ml in a litre
- Does not understand that when multiplying both sides of an equation by an amount
every term must be multiplied by the same amount
- source_sentence: 'Construct: Identify corresponding angles.
Question: M and N are the intersections of the line X Y with the lines P
Q and R S .
Which angle is corresponding to angle QMY? A pair of parallel lines pointing up
to the left. PQ and RS are the ends of the parallel lines. PQ is on the left of
the diagram with Q being the top left.
A red straight line, XY, crosses the parallel lines. X is on the left of the diagram.
Line XY crosses line PQ at a point marked M.
Line XY crosses line RS at a point marked N.
The angle QMY is marked in red.
Options:
A. XMP
B. SNY
C. SNX
D. XNR
Correct Answer: SNY
Incorrect Answer: XNR'
sentences:
- Confuses corresponding and alternate angles
- Estimates shares of a ratio instead of calculating
- Misremembers the quadratic formula
- source_sentence: 'Construct: Factorise a quadratic expression in the form x² -
c.
Question: Factorise this expression, if possible:
(
p^2-4
).
Options:
A. (p-2)(p+2)
B. p(p-2)
C. (p-2)(p-2)
D. Does not
factorise
Correct Answer: (p-2)(p+2)
Incorrect Answer: p(p-2)'
sentences:
- Mixes up greater than and less than symbols
- Does not know how to find the length of a line segment from coordinates
- Does not recognise difference of two squares
- source_sentence: 'Construct: Solve quadratic equations using the quadratic formula
where the coefficient of x² is not 1.
Question: Vera wants to solve this equation using the quadratic formula.
(
3 h^2-10 h+4=0
)
What should replace the circle? (? pm square root of (?-?) / bigcirc).
Options:
A. 3
B. 5
C. 9
D. 6
Correct Answer: 6
Incorrect Answer: 3'
sentences:
- Misremembers the quadratic formula
- When asked for a specific term in a sequence gives the term after
- Does not know that vertically opposite angles are equal
---
# 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 = [
'Construct: Solve quadratic equations using the quadratic formula where the coefficient of x² is not 1.\n\nQuestion: Vera wants to solve this equation using the quadratic formula.\n(\n3 h^2-10 h+4=0\n)\n\nWhat should replace the circle? (? pm square root of (?-?) / bigcirc).\n\nOptions:\nA. 3\nB. 5\nC. 9\nD. 6\n\nCorrect Answer: 6\n\nIncorrect Answer: 3',
'Misremembers the quadratic formula',
'Does not know that vertically opposite angles are equal',
]
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]
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Dataset
#### csv
* Dataset: csv
* Size: 2,442 training samples
* Columns: <code>qa_pair_text</code> and <code>MisconceptionName</code>
* Approximate statistics based on the first 1000 samples:
| | qa_pair_text | MisconceptionName |
|:--------|:-------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 40 tokens</li><li>mean: 102.66 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 15.26 tokens</li><li>max: 39 tokens</li></ul> |
* Samples:
| qa_pair_text | MisconceptionName |
|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------|
| <code>Construct: Convert between cm³ and mm³.<br><br>Question: 1 cm^3 is the same as _______ mm^3.<br><br>Options:<br>A. 10<br>B. 100<br>C. 1000<br>D. 10000<br><br>Correct Answer: 1000<br><br>Incorrect Answer: 10</code> | <code>Does not cube the conversion factor when converting cubed units</code> |
| <code>Construct: Write algebraic expressions with correct algebraic convention.<br><br>Question: Which answer shows the following calculation using the correct algebraic convention?<br>(<br>y x x+b x 3<br>).<br><br>Options:<br>A. y x+b 3<br>B. x y+3 b<br>C. y+3 b x<br>D. 3 b x y<br><br>Correct Answer: x y+3 b<br><br>Incorrect Answer: 3 b x y</code> | <code>Multiplies all terms together when simplifying an expression</code> |
| <code>Construct: Write algebraic expressions with correct algebraic convention.<br><br>Question: Which of the following is the correct way of writing: p divided by q , then add 3 using algebraic convention?<br><br>Options:<br>A. p q+3<br>B. (p / q)+3<br>C. (p / q+3)<br>D. p-q+3<br><br>Correct Answer: (p / q)+3<br><br>Incorrect Answer: p-q+3</code> | <code>Has used a subtraction sign to represent division</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim"
}
```
### Evaluation Dataset
#### csv
* Dataset: csv
* Size: 1,928 evaluation samples
* Columns: <code>qa_pair_text</code> and <code>MisconceptionName</code>
* Approximate statistics based on the first 1000 samples:
| | qa_pair_text | MisconceptionName |
|:--------|:-------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 40 tokens</li><li>mean: 103.34 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 14.34 tokens</li><li>max: 40 tokens</li></ul> |
* Samples:
| qa_pair_text | MisconceptionName |
|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------|
| <code>Construct: Multiply two decimals together with the same number of decimal places.<br><br>Question: 0.4^2=.<br><br>Options:<br>A. 0.08<br>B. 0.8<br>C. 1.6<br>D. 0.16<br><br>Correct Answer: 0.16<br><br>Incorrect Answer: 0.8</code> | <code>Mixes up squaring and multiplying by 2 or doubling</code> |
| <code>Construct: Calculate the cube root of a number.<br><br>Question: 3rd root of (8)=.<br><br>Options:<br>A. 2 . dot{6}<br>B. 4<br>C. 64<br>D. 2<br><br>Correct Answer: 2<br><br>Incorrect Answer: 4</code> | <code>Halves when asked to find the cube root</code> |
| <code>Construct: Calculate missing lengths of shapes by geometrical inference, where the lengths given are in the same units.<br><br>Question: What is the area of the shaded section of this composite shape made from rectangles? A composite shape made from two rectangles that form an "L" shape. The base of the shape is horizontal and is 13cm long. The vertical height of the whole shape is 14cm. The horizontal width of the top part of the shape is 6cm. The vertical height of the top rectangle is 8cm. The right handed rectangle is shaded blue.<br><br>Options:<br>A. 48 cm^2<br>B. 104 cm^2<br>C. 42 cm^2<br>D. 56 cm^2<br><br>Correct Answer: 42 cm^2<br><br>Incorrect Answer: 48 cm^2</code> | <code>Uses an incorrect side length when splitting a composite shape into parts</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](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
- `eval_strategy`: steps
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `gradient_accumulation_steps`: 32
- `weight_decay`: 0.01
- `num_train_epochs`: 20
- `lr_scheduler_type`: cosine_with_restarts
- `warmup_ratio`: 0.1
- `fp16`: True
- `load_best_model_at_end`: True
- `gradient_checkpointing`: 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`: 32
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 5e-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`: 20
- `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`: 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`: True
- `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 | loss |
|:----------:|:------:|:-------------:|:----------:|
| 0.4183 | 2 | 1.2854 | - |
| 0.6275 | 3 | - | 1.0368 |
| 0.8366 | 4 | 1.0855 | - |
| 1.2549 | 6 | 0.7559 | 0.8548 |
| 1.6732 | 8 | 0.7032 | - |
| 1.8824 | 9 | - | 0.6840 |
| 2.0915 | 10 | 0.474 | - |
| 2.5098 | 12 | 0.3959 | 0.6023 |
| 2.9281 | 14 | 0.3279 | - |
| 3.1373 | 15 | - | 0.5576 |
| 3.3464 | 16 | 0.2164 | - |
| **3.7647** | **18** | **0.1991** | **0.4972** |
| 4.1830 | 20 | 0.1378 | - |
| 4.3922 | 21 | - | 0.5081 |
| 4.6013 | 22 | 0.1168 | - |
| 5.0196 | 24 | 0.0955 | 0.5000 |
* The bold row denotes the saved checkpoint.
### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.1.1
- Transformers: 4.44.2
- PyTorch: 2.4.1+cu121
- Accelerate: 0.34.2
- 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",
}
```
#### MultipleNegativesRankingLoss
```bibtex
@misc{henderson2017efficient,
title={Efficient Natural Language Response Suggestion for Smart Reply},
author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
year={2017},
eprint={1705.00652},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
<!--
## Glossary
*Clearly define terms in order to be accessible across audiences.*
-->
<!--
## Model Card Authors
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->
<!--
## Model Card Contact
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
--> |