SentenceTransformer

This is a sentence-transformers model trained. 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
Maximum Sequence Length: 512 tokens
Output Dimensionality: 1024 tokens
Similarity Function: Cosine Similarity

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': 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})
)

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/gte-base-eedi-2024")
# Run inference
sentences = [
    'believes percentages cannot be converted into fractions\n\nconvert two digit integer percentages to fractions(converting between fractions and percentages).\nquestion: convert this percentage to a fraction\n 62 \\% \ncorrect answer: \\frac{31}{50} \nincorrect answer: none of these',
    'Does not understand a percentage is out of 100',
    'Believes the gradients of perpendicular lines are reciprocals of the same sign',
]
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: 218,496 training samples
Columns: FullText, GroundTruthMisconception, and PredictMisconception

Approximate statistics based on the first 1000 samples:

	FullText	GroundTruthMisconception	PredictMisconception
type	string	string	string
details	min: 58 tokens mean: 95.45 tokens max: 159 tokens	min: 8 tokens mean: 17.0 tokens max: 38 tokens	min: 6 tokens mean: 15.27 tokens max: 40 tokens

Samples:

FullText	GroundTruthMisconception	PredictMisconception
`believes that the order of operations does not affect the answer to a calculation use the order of operations to carry out calculations involving powers(bidmas). question: [ 3 \times 2+4-5 ] where do the brackets need to go to make the answer equal 13 ? correct answer: 3 \times(2+4)-5 incorrect answer: does not need brackets`	`Confuses the order of operations, believes addition comes before multiplication`	`Believes infinite gradient is not possible in real life.`
`believes that the order of operations does not affect the answer to a calculation use the order of operations to carry out calculations involving powers(bidmas). question: [ 3 \times 2+4-5 ] where do the brackets need to go to make the answer equal 13 ? correct answer: 3 \times(2+4)-5 incorrect answer: does not need brackets`	`Confuses the order of operations, believes addition comes before multiplication`	`Struggles to draw 3D shapes on isometric paper`
`believes that the order of operations does not affect the answer to a calculation use the order of operations to carry out calculations involving powers(bidmas). question: [ 3 \times 2+4-5 ] where do the brackets need to go to make the answer equal 13 ? correct answer: 3 \times(2+4)-5 incorrect answer: does not need brackets`	`Confuses the order of operations, believes addition comes before multiplication`	`Believes an upward slope on a distance-time graph means travelling back towards the starting point.`

Loss: MultipleNegativesRankingLoss with these parameters:

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

Training Hyperparameters

Non-Default Hyperparameters

gradient_accumulation_steps: 16
eval_accumulation_steps: 16
learning_rate: 2e-05
weight_decay: 0.01
num_train_epochs: 2
lr_scheduler_type: cosine_with_restarts
warmup_ratio: 0.1
fp16: True
batch_sampler: no_duplicates

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: 16
eval_accumulation_steps: 16
learning_rate: 2e-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: 2
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: 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
batch_sampler: no_duplicates
multi_dataset_batch_sampler: proportional

Training Logs

Epoch	Step	Training Loss
0.1757	300	0.9143
0.3515	600	0.8284
0.5272	900	0.8444
0.7030	1200	0.7999
0.8787	1500	0.8901
1.0512	1800	0.8029
1.2269	2100	0.6198
1.4027	2400	0.5837
1.5784	2700	0.603
1.7542	3000	0.5336
1.9299	3300	0.5977

Framework Versions

Python: 3.12.3
Sentence Transformers: 3.1.0
Transformers: 4.42.3
PyTorch: 2.3.0+cu121
Accelerate: 0.32.1
Datasets: 3.0.0
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}
}