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---
language: []
library_name: sentence-transformers
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:800
- loss:TripletLoss
base_model: sentence-transformers/all-mpnet-base-v2
datasets: []
widget:
- source_sentence: What is the advice given about the use of color in dataviz?
sentences:
- Don't use color if they communicate nothing.
- Four problems with Pie Charts are detailed in a guide by iCharts.net.
- Always use bright colors for highlighting important data.
- source_sentence: What is the effect of a large sample size on the use of jitter
in a boxplot?
sentences:
- A large sample size will enhance the use of jitter in a boxplot.
- If you have a large sample size, using jitter is not an option anymore since dots
will overlap, making the figure uninterpretable.
- It is a good practice to use small multiples.
- source_sentence: What is a suitable usage of pie charts in data visualization?
sentences:
- If you have a single series to display and all quantitative variables have the
same scale, then use a barplot or a lollipop plot, ranking the variables.
- Pie charts rapidly show parts to a whole better than any other plot. They are
most effective when used to compare parts to the whole.
- Pie charts are a flawed chart which can sometimes be justified if the differences
between groups are large.
- source_sentence: Where can a note on long labels be found?
sentences:
- https://www.data-to-viz.com/caveat/hard_label.html
- A pie chart can tell a story very well; that all the data points as a percentage
of the whole are very similar.
- https://twitter.com/r_graph_gallery?lang=en
- source_sentence: What is the reason pie plots can work as well as bar plots in some
scenarios?
sentences:
- Pie plots can work well for comparing portions a whole or portions one another,
especially when dealing with a single digit count of items.
- https://www.r-graph-gallery.com/line-plot/ and https://python-graph-gallery.com/line-chart/
- Thanks for your comment Tom, I do agree with you.
pipeline_tag: sentence-similarity
---
# SentenceTransformer based on sentence-transformers/all-mpnet-base-v2
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/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](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) <!-- at revision 84f2bcc00d77236f9e89c8a360a00fb1139bf47d -->
- **Maximum Sequence Length:** 384 tokens
- **Output Dimensionality:** 768 tokens
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **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': 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:
```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("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]
```
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## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 800 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive | negative |
|:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 7 tokens</li><li>mean: 15.26 tokens</li><li>max: 41 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 23.25 tokens</li><li>max: 306 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 16.38 tokens</li><li>max: 57 tokens</li></ul> |
* Samples:
| anchor | positive | negative |
|:-------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------|
| <code>Did you ever figure out a solution to the error message problem when using your own data?</code> | <code>Yes, a solution was found. You have to add ' group = name ' inside the ' ggplot(aes())' like ggplot(aes(x=year, y=n,group=name)).</code> | <code>I recommend sorting by some feature of the data, instead of in alphabetical order of the names.</code> |
| <code>Why should you consider reordering your data when building a chart?</code> | <code>Reordering your data can help in better visualization. Sometimes the order of groups must be set by their features and not their values.</code> | <code>You should reorder your data to clean it.</code> |
| <code>What is represented on the X-axis of the chart?</code> | <code>The price ranges cut in several 10 euro bins.</code> | <code>The number of apartments per bin.</code> |
* Loss: [<code>TripletLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#tripletloss) with these parameters:
```json
{
"distance_metric": "TripletDistanceMetric.EUCLIDEAN",
"triplet_margin": 5
}
```
### Evaluation Dataset
#### Unnamed Dataset
* Size: 200 evaluation samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive | negative |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 8 tokens</li><li>mean: 14.99 tokens</li><li>max: 36 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 22.38 tokens</li><li>max: 96 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 16.08 tokens</li><li>max: 58 tokens</li></ul> |
* Samples:
| anchor | positive | negative |
|:--------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------|
| <code>What can be inferred about group C and B from the jittered boxplot?</code> | <code>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.</code> | <code>Group C has the largest sample size and Group B has dots evenly distributed.</code> |
| <code>What can cause a reduction in computing time and help avoid overplotting when dealing with data?</code> | <code>Plotting only a fraction of your data can cause a reduction in computing time and help avoid overplotting.</code> | <code>Plotting all of your data is the best method to reduce computing time.</code> |
| <code>How can area charts be used for data visualization?</code> | <code>Area charts can be used to give a more general overview of the dataset, especially when used in combination with small multiples.</code> | <code>Area charts make it obvious to spot a particular group in a crowded data visualization.</code> |
* Loss: [<code>TripletLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#tripletloss) with these parameters:
```json
{
"distance_metric": "TripletDistanceMetric.EUCLIDEAN",
"triplet_margin": 5
}
```
### 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
<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
- `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
</details>
### 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}
}
```
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