Add new SentenceTransformer model.

e8156cc verified 5 months ago

19.7 kB

	---
	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]
	```

	<!--
	### Direct Usage (Transformers)

	<details><summary>Click to see the direct usage in Transformers</summary>

	</details>
	-->

	<!--
	### Downstream Usage (Sentence Transformers)

	You can finetune this model on your own dataset.

	<details><summary>Click to expand</summary>

	</details>
	-->

	<!--
	### Out-of-Scope Use

	List how the model may foreseeably be misused and address what users ought not to do with the model.
	-->

	<!--
	## Bias, Risks and Limitations

	What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.
	-->

	<!--
	### Recommendations

	What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.
	-->

	## 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}
	}
	```

	<!--
	## Glossary

	Clearly define terms in order to be accessible across audiences.
	-->

	<!--
	## Model Card Authors

	Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.
	-->

	<!--
	## Model Card Contact

	Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.
	-->