This is a sentence-transformers model finetuned from sentence-transformers/all-mpnet-base-v2. 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/all-mpnet-base-v2
Maximum Sequence Length: 384 tokens
Output Dimensionality: 768 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': 384, '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})
  (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.

python

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("edubm/vis-sim-triplets-mpnet")
# Run inference
sentences = [
    'What is the reason pie plots can work as well as bar plots in some scenarios?',
    'Pie plots can work well for comparing portions a whole or portions one another, especially when dealing with a single digit count of items.',
    'Thanks for your comment Tom, I do agree with you.',
]
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

Unnamed Dataset

Size: 800 training samples
Columns: anchor, positive, and negative
Approximate statistics based on the first 1000 samples:
anchor positive negative
type string string string
details
min: 7 tokens
mean: 15.26 tokens
max: 41 tokens
min: 3 tokens
mean: 23.25 tokens
max: 306 tokens
min: 3 tokens
mean: 16.38 tokens
max: 57 tokens

Samples:

anchor	positive	negative
`Did you ever figure out a solution to the error message problem when using your own data?`	`Yes, a solution was found. You have to add ' group = name ' inside the ' ggplot(aes())' like ggplot(aes(x=year, y=n,group=name)).`	`I recommend sorting by some feature of the data, instead of in alphabetical order of the names.`
`Why should you consider reordering your data when building a chart?`	`Reordering your data can help in better visualization. Sometimes the order of groups must be set by their features and not their values.`	`You should reorder your data to clean it.`
`What is represented on the X-axis of the chart?`	`The price ranges cut in several 10 euro bins.`	`The number of apartments per bin.`

Loss: TripletLoss with these parameters:
json
1{ 2 "distance_metric": "TripletDistanceMetric.EUCLIDEAN", 3 "triplet_margin": 5 4}

Evaluation Dataset

Unnamed Dataset

Size: 200 evaluation samples
Columns: anchor, positive, and negative
Approximate statistics based on the first 1000 samples:
anchor positive negative
type string string string
details
min: 8 tokens
mean: 14.99 tokens
max: 36 tokens
min: 3 tokens
mean: 22.38 tokens
max: 96 tokens
min: 3 tokens
mean: 16.08 tokens
max: 58 tokens

Samples:

anchor	positive	negative
`What can be inferred about group C and B from the jittered boxplot?`	`Group C has a small sample size compared to the other groups. Group B seems to have a bimodal distribution with dots distributed in 2 groups: around y=18 and y=13.`	`Group C has the largest sample size and Group B has dots evenly distributed.`
`What can cause a reduction in computing time and help avoid overplotting when dealing with data?`	`Plotting only a fraction of your data can cause a reduction in computing time and help avoid overplotting.`	`Plotting all of your data is the best method to reduce computing time.`
`How can area charts be used for data visualization?`	`Area charts can be used to give a more general overview of the dataset, especially when used in combination with small multiples.`	`Area charts make it obvious to spot a particular group in a crowded data visualization.`

Loss: TripletLoss with these parameters:
json
1{ 2 "distance_metric": "TripletDistanceMetric.EUCLIDEAN", 3 "triplet_margin": 5 4}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 16
per_device_eval_batch_size: 16
num_train_epochs: 1
warmup_ratio: 0.1
fp16: True

All Hyperparameters

Click to expand

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
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: 1
max_steps: -1
lr_scheduler_type: linear
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
batch_sampler: batch_sampler
multi_dataset_batch_sampler: proportional

Training Logs

Epoch	Step	Training Loss	loss
0.02	1	4.8436	4.8922
0.04	2	4.9583	4.8904
0.06	3	4.8262	4.8862
0.08	4	4.8961	4.8820
0.1	5	4.9879	4.8754
0.12	6	4.8599	4.8680
0.14	7	4.9098	4.8586
0.16	8	4.8802	4.8496
0.18	9	4.8797	4.8392
0.2	10	4.8691	4.8307
0.22	11	4.9213	4.8224
0.24	12	4.88	4.8145
0.26	13	4.9131	4.8071
0.28	14	4.7596	4.8004
0.3	15	4.8388	4.7962
0.32	16	4.8434	4.7945
0.34	17	4.8726	4.7939
0.36	18	4.8049	4.7943
0.38	19	4.8225	4.7932
0.4	20	4.7631	4.7900
0.42	21	4.7841	4.7847
0.44	22	4.8077	4.7759
0.46	23	4.7731	4.7678
0.48	24	4.7623	4.7589
0.5	25	4.8572	4.7502
0.52	26	4.843	4.7392
0.54	27	4.6826	4.7292
0.56	28	4.7584	4.7180
0.58	29	4.7281	4.7078
0.6	30	4.7491	4.6982
0.62	31	4.7501	4.6897
0.64	32	4.6219	4.6826
0.66	33	4.7323	4.6768
0.68	34	4.5499	4.6702
0.7	35	4.7682	4.6648
0.72	36	4.6483	4.6589
0.74	37	4.6675	4.6589
0.76	38	4.7389	4.6527
0.78	39	4.7721	4.6465
0.8	40	4.6043	4.6418
0.82	41	4.7894	4.6375
0.84	42	4.6134	4.6341
0.86	43	4.6664	4.6307
0.88	44	4.5249	4.6264
0.9	45	4.7045	4.6227
0.92	46	4.7231	4.6198
0.94	47	4.7011	4.6176
0.96	48	4.5876	4.6159
0.98	49	4.7567	4.6146
1.0	50	4.6706	4.6138

Framework Versions

Python: 3.10.12
Sentence Transformers: 3.0.1
Transformers: 4.41.2
PyTorch: 2.3.0+cu121
Accelerate: 0.31.0
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",
}

TripletLoss

bibtex

@misc{hermans2017defense,
    title={In Defense of the Triplet Loss for Person Re-Identification}, 
    author={Alexander Hermans and Lucas Beyer and Bastian Leibe},
    year={2017},
    eprint={1703.07737},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}

	anchor	positive	negative
type	string	string	string
details	min: 7 tokens mean: 15.26 tokens max: 41 tokens	min: 3 tokens mean: 23.25 tokens max: 306 tokens	min: 3 tokens mean: 16.38 tokens max: 57 tokens