SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2

This is a sentence-transformers model finetuned from sentence-transformers/all-MiniLM-L6-v2 on the csv dataset. It maps sentences & paragraphs to a 384-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/all-MiniLM-L6-v2
Maximum Sequence Length: 256 tokens
Output Dimensionality: 384 tokens
Similarity Function: Cosine Similarity
Training Dataset:
- csv

Model Sources

Documentation: Sentence Transformers Documentation
Repository: Sentence Transformers on GitHub
Hugging Face: Sentence Transformers on Hugging Face

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 384, '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})
  (2): Normalize()
)

Usage

Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

pip install -U sentence-transformers

Then you can load this model and run inference.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("Gurveer05/all-MiniLM-eedi-2024")
# Run inference
sentences = [
    'Construct:  Solve coordinate geometry questions involving ratio.\n\nQuestion:  A straight line on squared paper. Points P, Q and R lie on this line. The leftmost end of the line is labelled P. If you travel right 4 squares and up 1 square you get to point Q. If you then travel 8 squares right and 2 squares up from Q you reach point R. What is the ratio of  P Q: P R  ?\n\nOptions:\nA. 1: 12\nB. 1: 4\nC. 1: 2\nD. 1: 3\n\nCorrect Answer: 1: 3\n\nIncorrect Answer: 1: 2\n\nPredicted Misconception: Misunderstanding the ratio calculation by not considering the correct horizontal and vertical distances between points P, Q, and R.',
    'May have estimated when using ratios with geometry',
    'Thinks x = y is an axis',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# 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: 12,210 training samples
Columns: qa_pair_text, MisconceptionName, and negative

Approximate statistics based on the first 1000 samples:

	qa_pair_text	MisconceptionName	negative
type	string	string	string
details	min: 54 tokens mean: 121.45 tokens max: 256 tokens	min: 4 tokens mean: 15.16 tokens max: 39 tokens	min: 7 tokens mean: 14.49 tokens max: 40 tokens

Samples:

qa_pair_text	MisconceptionName	negative
`Construct: Construct frequency tables. Question: Dave has recorded the number of pets his classmates have in the frequency table on the right. Number of pets`	Frequency 0	4 1
`Construct: Convert between any other time periods. Question: To work out how many hours in a year you could do... Options: A. 365 x 7 B. 365 x 60 C. 365 x 12 D. 365 x 24 Correct Answer: 365 x 24 Incorrect Answer: 365 x 60 Predicted Misconception: Multiplying days by hours per minute instead of hours per day.`	`Answers as if there are 60 hours in a day`	`Confuses an equation with an expression`
`Construct: Given information about one part, work out other parts. Question: Jess and Heena share some sweets in the ratio 3;: 5 . Jess gets 15 sweets. How many sweets does Heena get? Options: A. 17 B. 9 C. 5 D. 25 Correct Answer: 25 Incorrect Answer: 17 Predicted Misconception: Misunderstanding the direct proportionality between the ratio and actual quantities.`	`Thinks a difference of one part in a ratio means the quantities will differ by one unit`	`Believes dividing two positives will give a negative answer`

Loss: MultipleNegativesRankingLoss with these parameters:

{
    "scale": 20.0,
    "similarity_fct": "cos_sim"
}

Evaluation Dataset

csv

Dataset: csv
Size: 9,640 evaluation samples
Columns: qa_pair_text, MisconceptionName, and negative

Approximate statistics based on the first 1000 samples:

	qa_pair_text	MisconceptionName	negative
type	string	string	string
details	min: 56 tokens mean: 119.35 tokens max: 256 tokens	min: 6 tokens mean: 14.51 tokens max: 39 tokens	min: 6 tokens mean: 13.86 tokens max: 40 tokens

Samples:

qa_pair_text	MisconceptionName	negative
Construct: Identify when rounding a calculation will give an over or under approximation. Question: Tom and Katie are discussing how to estimate the answer to [ 38.8745 / 7.9302 ] Tom says 40 / 7.9302 would give an overestimate. Katie says 38.8745 / 8 would give an overestimate. Who is correct? Options: A. Only Tom B. Only Katie C. Both Tom and Katie D. Neither is correct Correct Answer: Only Tom Incorrect Answer: Neither is correct Predicted Misconception: Rounding both numbers up leads to an overestimate.	`Believes that the larger the dividend, the smaller the answer.`	`Does not know how to calculate the mean`
`Construct: Substitute negative integer values into expressions involving no powers or roots. Question: Amy is trying to work out the distance between these two points: (1,-6) and (-5,2) She labels them like this: x_1`	y_1	x_2
`Construct: Round numbers to three or more decimal places. Question: What is 20.15349 rounded to 3 decimal places? Options: A. 20.153 B. 20.15 C. 20.154 D. 20.253 Correct Answer: 20.153 Incorrect Answer: 20.154 Predicted Misconception: Rounding up the fourth decimal place without considering the fifth decimal place.`	`Rounds up instead of down`	`When dividing decimals, does not realize that the order and position of the digits (relative to each other) has to remain constant.`

Loss: MultipleNegativesRankingLoss with these parameters:

{
    "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
gradient_accumulation_steps: 8
learning_rate: 1e-05
weight_decay: 0.01
num_train_epochs: 40
lr_scheduler_type: cosine
lr_scheduler_kwargs: {'num_cycles': 20}
warmup_ratio: 0.1
fp16: True
load_best_model_at_end: True
gradient_checkpointing: True
gradient_checkpointing_kwargs: {'use_reentrant': False}
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: 8
eval_accumulation_steps: None
torch_empty_cache_steps: None
learning_rate: 1e-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: 40
max_steps: -1
lr_scheduler_type: cosine
lr_scheduler_kwargs: {'num_cycles': 20}
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: {'use_reentrant': False}
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

Training Logs

Epoch	Step	Training Loss	loss
0.5026	12	2.2789	-
1.0052	24	2.1642	1.9746
1.4974	36	2.0463	-
2.0	48	1.8955	1.6808
2.4921	60	1.7692	-
2.9948	72	1.6528	1.4532
3.4869	84	1.5298	-
3.9895	96	1.4338	1.2853
4.4817	108	1.3374	-
4.9843	120	1.3084	1.2465
5.4764	132	1.2921	-
5.9791	144	1.2143	1.1766
6.4712	156	1.1689	-
6.9738	168	1.1656	1.1518
7.4660	180	1.1172	-
7.9686	192	1.0737	1.1080
8.4607	204	1.0373	-
8.9634	216	1.0445	1.0874
9.4555	228	0.9707	-
9.9581	240	0.9644	1.0649
10.4503	252	0.9252	-
10.9529	264	0.9211	1.0367
11.4450	276	0.8645	-
11.9476	288	0.8635	1.0297
12.4398	300	0.8279	-
12.9424	312	0.819	1.0161
13.4346	324	0.7684	-
13.9372	336	0.7842	1.0016
14.4293	348	0.7448	-
14.9319	360	0.7321	0.9951
15.4241	372	0.7064	-
15.9267	384	0.7161	0.9835
16.4188	396	0.6692	-
16.9215	408	0.6594	0.9774
17.4136	420	0.6405	-
17.9162	432	0.638	0.9723
18.4084	444	0.6	-
18.9110	456	0.6122	0.9706
19.4031	468	0.5763	-
19.9058	480	0.5787	0.9732
20.3979	492	0.5432	-
20.9005	504	0.5599	0.9618
21.3927	516	0.5245	-
21.8953	528	0.5278	0.9626
22.3874	540	0.4989	-
22.8901	552	0.509	0.9583
23.3822	564	0.4674	-
23.8848	576	0.4854	0.9573
24.3770	588	0.4619	-
24.8796	600	0.4631	0.9615
25.3717	612	0.4339	-
25.8743	624	0.4427	0.9593
26.3665	636	0.4225	-
26.8691	648	0.4245	0.9694
27.3613	660	0.3936	-
27.8639	672	0.4168	0.9586
28.3560	684	0.3835	-
28.8586	696	0.3921	0.9629

The bold row denotes the saved checkpoint.

Framework Versions

Python: 3.10.14
Sentence Transformers: 3.1.1
Transformers: 4.44.0
PyTorch: 2.4.0
Accelerate: 0.33.0
Datasets: 2.19.2
Tokenizers: 0.19.1

Citation

BibTeX

Sentence Transformers

@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

@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}
}