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---
language: []
library_name: sentence-transformers
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:300000
- loss:DenoisingAutoEncoderLoss
base_model: intfloat/e5-base-unsupervised
datasets: []
metrics:
- pearson_cosine
- spearman_cosine
- pearson_manhattan
- spearman_manhattan
- pearson_euclidean
- spearman_euclidean
- pearson_dot
- spearman_dot
- pearson_max
- spearman_max
widget:
- source_sentence: One mole of a substance of substance such atoms or). The is known
or Avogadro's constant
sentences:
- how effective are birth control pills and pulling out?
- can pvc be phthalate free?
- One mole of a substance is equal to 6.022 × 10²³ units of that substance (such
as atoms, molecules, or ions). The number 6.022 × 10²³ is known as Avogadro's
number or Avogadro's constant.
- source_sentence: is the difference between disability broadly defined a or to be
significantly impaired relative to the standard an individual group . To the term
disabled still just more, this or function
sentences:
- 'how to open pkf format? On a Windows PC, right-click the file, click "Properties",
then look under “Type of File.” On a Mac computer, right-click the file, click
“More Info,” then look under “Kind”. Tip: If it''s the PKF file extension, it
probably falls under the Audio Files type, so any program used for Audio Files
should open your PKF file.'
- When someone dreams you died, it means that whatever you mean to that person's
psychological state of mind 'has ended' or 'is absent'. ... People dream of dead
people because they miss something about them that was very strong emotionally
present when they were there, yet is missing in their daily-life now.
- what is the difference between disability and disabled? A disability is broadly
defined as a condition or function judged to be significantly impaired relative
to the usual standard of an individual or group. ... To most people today the
term "disabled" still means just that, and, more broadly, means "unable to perform"
this or that physical or mental function.
- source_sentence: how you contagious when
sentences:
- how long are you contagious when you have rsv?
- With WiFi on your camera you establish a wireless connection between your camera
and your phone, tablet, computer, or printer. It's also possible to connect two
cameras with each other via WiFi. The camera has its own WiFi network that transmits
signals.
- So, what does it mean when a guy looks you up and down? It will often mean that
he is checking you out especially if he only does it to you and he shows other
signs of attraction when around you. It can also be that he is initially observing
to see if you're a threat or that he is observing your outfit.
- source_sentence: you light east while is you can the of the . understanding The
on left is basically fajr time black you
sentences:
- A future - contract to buy (or sell) something in the future. An option - right
BUT NOT the obligation to buy (or sell) something in the future. A swap - two
parties exchanging something at agreed points in time. This could be an exchange
of currencies, of returns on assets, of different interest rate returns, etc..
- can i connect my iphone to my windows laptop? You can sync an iPhone with a Windows
10 computer wirelessly (over your local WiFi network) or via the Lightning cable.
... Open iTunes in Windows 10. Plug your iPhone (or iPad or iPod) into the computer
using a Lightning cable (or older 30-pin connector). Click on Device in iTunes
and choose your iPhone.
- 'Yes, Fajr is when you can see the light in the east while Sunrise is when you
can see the disk of the sun. For those who have a trouble understanding: The blue
area on the left is basically fajr time. The black area is when you can eat.'
- source_sentence: should eat diarrhea should solid as soon able you're bottle your
have, try to them as . at home until 48 last spreading others.
sentences:
- which countries were not affected by world war 2? There were eight countries that
declared neutrality; Portugal, Switzerland, Spain, Sweden, The Vatican, Andorra,
Ireland and Liechtenstein. However, all of these countries were still involved
in small ways.
- how to copy multiple cells in excel and paste?
- how long should you wait to eat after having diarrhea? You should eat solid food
as soon as you feel able to. If you're breastfeeding or bottle feeding your baby
and they have diarrhoea, you should try to feed them as normal. Stay at home until
at least 48 hours after the last episode of diarrhoea to prevent spreading any
infection to others.
pipeline_tag: sentence-similarity
model-index:
- name: SentenceTransformer based on intfloat/e5-base-unsupervised
results:
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts test
type: sts-test
metrics:
- type: pearson_cosine
value: 0.7707098586060571
name: Pearson Cosine
- type: spearman_cosine
value: 0.7583632499035035
name: Spearman Cosine
- type: pearson_manhattan
value: 0.7590199401674214
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.747524480818435
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.760482148803808
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.7488744991502696
name: Spearman Euclidean
- type: pearson_dot
value: 0.5774036226110284
name: Pearson Dot
- type: spearman_dot
value: 0.5600384269062831
name: Spearman Dot
- type: pearson_max
value: 0.7707098586060571
name: Pearson Max
- type: spearman_max
value: 0.7583632499035035
name: Spearman Max
---
# SentenceTransformer based on intfloat/e5-base-unsupervised
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [intfloat/e5-base-unsupervised](https://huggingface.co./intfloat/e5-base-unsupervised). 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:** [intfloat/e5-base-unsupervised](https://huggingface.co./intfloat/e5-base-unsupervised) <!-- at revision 6003a5b7ce770b0549203e41115b9fc683f16dad -->
- **Maximum Sequence Length:** 512 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': 512, 'do_lower_case': False}) with Transformer model: BertModel
(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})
)
```
## 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("bobox/E5-base-unsupervised-TSDAE")
# Run inference
sentences = [
"should eat diarrhea should solid as soon able you're bottle your have, try to them as . at home until 48 last spreading others.",
"how long should you wait to eat after having diarrhea? You should eat solid food as soon as you feel able to. If you're breastfeeding or bottle feeding your baby and they have diarrhoea, you should try to feed them as normal. Stay at home until at least 48 hours after the last episode of diarrhoea to prevent spreading any infection to others.",
'how to copy multiple cells in excel and paste?',
]
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>
-->
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### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
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## Evaluation
### Metrics
#### Semantic Similarity
* Dataset: `sts-test`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.7707 |
| **spearman_cosine** | **0.7584** |
| pearson_manhattan | 0.759 |
| spearman_manhattan | 0.7475 |
| pearson_euclidean | 0.7605 |
| spearman_euclidean | 0.7489 |
| pearson_dot | 0.5774 |
| spearman_dot | 0.56 |
| pearson_max | 0.7707 |
| spearman_max | 0.7584 |
<!--
## 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: 300,000 training samples
* Columns: <code>sentence_0</code> and <code>sentence_1</code>
* Approximate statistics based on the first 1000 samples:
| | sentence_0 | sentence_1 |
|:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 3 tokens</li><li>mean: 20.46 tokens</li><li>max: 69 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 47.85 tokens</li><li>max: 132 tokens</li></ul> |
* Samples:
| sentence_0 | sentence_1 |
|:-------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>matter An unit of retains all subatomic neutrons Hydrogen (one one neutrons</code> | <code>are particles of matter atoms? An atom is the smallest unit of matter that retains all of the chemical properties of an element. ... Most atoms contain all three of these types of subatomic particles—protons, electrons, and neutrons. Hydrogen (H) is an exception because it typically has one proton and one electron, but no neutrons.</code> |
| <code>equals how</code> | <code>5 ml equals how many ounces?</code> |
| <code>"A Country Boy School is poor is forced to its boy to school following official, ignoring mean a jail</code> | <code>"A Country Boy Quits School" by Lao Hsiang is an endearing social satire. It is about a poor Chinese family which is forced to send its boy to school following an official proclamation, ignoring which would mean a jail term.</code> |
* Loss: [<code>DenoisingAutoEncoderLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#denoisingautoencoderloss)
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 14
- `per_device_eval_batch_size`: 14
- `num_train_epochs`: 1
- `multi_dataset_batch_sampler`: round_robin
#### 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`: 14
- `per_device_eval_batch_size`: 14
- `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
- `num_train_epochs`: 1
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.0
- `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`: False
- `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`: round_robin
</details>
### Training Logs
| Epoch | Step | Training Loss | sts-test_spearman_cosine |
|:------:|:-----:|:-------------:|:------------------------:|
| 0 | 0 | - | 0.7211 |
| 0.0233 | 500 | 6.3144 | - |
| 0.0467 | 1000 | 5.3949 | - |
| 0.0500 | 1072 | - | 0.6820 |
| 0.0700 | 1500 | 5.0531 | - |
| 0.0933 | 2000 | 4.8547 | - |
| 0.1001 | 2144 | - | 0.7126 |
| 0.1167 | 2500 | 4.7058 | - |
| 0.1400 | 3000 | 4.5771 | - |
| 0.1501 | 3216 | - | 0.7290 |
| 0.1633 | 3500 | 4.4591 | - |
| 0.1867 | 4000 | 4.3502 | - |
| 0.2001 | 4288 | - | 0.7351 |
| 0.2100 | 4500 | 4.3071 | - |
| 0.2333 | 5000 | 4.2042 | - |
| 0.2501 | 5360 | - | 0.7464 |
| 0.2567 | 5500 | 4.1657 | - |
| 0.2800 | 6000 | 4.1111 | - |
| 0.3002 | 6432 | - | 0.7492 |
| 0.3033 | 6500 | 4.045 | - |
| 0.3267 | 7000 | 4.017 | - |
| 0.3500 | 7500 | 3.9651 | - |
| 0.3502 | 7504 | - | 0.7554 |
| 0.3733 | 8000 | 3.9199 | - |
| 0.3967 | 8500 | 3.8691 | - |
| 0.4002 | 8576 | - | 0.7517 |
| 0.4200 | 9000 | 3.8563 | - |
| 0.4433 | 9500 | 3.815 | - |
| 0.4502 | 9648 | - | 0.7540 |
| 0.4667 | 10000 | 3.7892 | - |
| 0.4900 | 10500 | 3.7543 | - |
| 0.5003 | 10720 | - | 0.7585 |
| 0.5133 | 11000 | 3.7391 | - |
| 0.5367 | 11500 | 3.7442 | - |
| 0.5503 | 11792 | - | 0.7587 |
| 0.5600 | 12000 | 3.7187 | - |
| 0.5833 | 12500 | 3.6855 | - |
| 0.6003 | 12864 | - | 0.7572 |
| 0.6067 | 13000 | 3.6751 | - |
| 0.6300 | 13500 | 3.6373 | - |
| 0.6503 | 13936 | - | 0.7574 |
| 0.6533 | 14000 | 3.6292 | - |
| 0.6767 | 14500 | 3.6277 | - |
| 0.7000 | 15000 | 3.6084 | - |
| 0.7004 | 15008 | - | 0.7575 |
| 0.7233 | 15500 | 3.6103 | - |
| 0.7467 | 16000 | 3.5953 | - |
| 0.7504 | 16080 | - | 0.7576 |
| 0.7700 | 16500 | 3.6232 | - |
| 0.7933 | 17000 | 3.5741 | - |
| 0.8004 | 17152 | - | 0.7583 |
| 0.8167 | 17500 | 3.5639 | - |
| 0.8400 | 18000 | 3.5667 | - |
| 0.8504 | 18224 | - | 0.7589 |
| 0.8633 | 18500 | 3.5598 | - |
| 0.8866 | 19000 | 3.5636 | - |
| 0.9005 | 19296 | - | 0.7584 |
| 0.9100 | 19500 | 3.5536 | - |
| 0.9333 | 20000 | 3.5529 | - |
| 0.9505 | 20368 | - | 0.7584 |
| 0.9566 | 20500 | 3.5485 | - |
| 0.9800 | 21000 | 3.5503 | - |
| 1.0 | 21429 | - | 0.7584 |
### Framework Versions
- Python: 3.10.13
- Sentence Transformers: 3.0.1
- Transformers: 4.41.2
- PyTorch: 2.1.2
- 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",
}
```
#### DenoisingAutoEncoderLoss
```bibtex
@inproceedings{wang-2021-TSDAE,
title = "TSDAE: Using Transformer-based Sequential Denoising Auto-Encoderfor Unsupervised Sentence Embedding Learning",
author = "Wang, Kexin and Reimers, Nils and Gurevych, Iryna",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2021",
month = nov,
year = "2021",
address = "Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
pages = "671--688",
url = "https://arxiv.org/abs/2104.06979",
}
```
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