---
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)
- **Maximum Sequence Length:** 384 tokens
- **Output Dimensionality:** 768 tokens
- **Similarity Function:** Cosine Similarity
### 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]
```
## 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](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: 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](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
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