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mpnet_MH_embedding

Feature Extraction
sentence-transformers
Safetensors
mpnet
sentence-similarity
dense
Generated from Trainer
dataset_size:4615
loss:TripletLoss
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mpnet_MH_embedding / README.md
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Add new SentenceTransformer model.
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tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dense
- generated_from_trainer
- dataset_size:4615
- loss:TripletLoss
base_model: sentence-transformers/all-mpnet-base-v2
widget:
- source_sentence: Do you ever feel like you have failed in life or let yourself down?
sentences:
- But I just don't feel like even getting started because I know that I will fail
again.
- I cant remember the last time I felt happiness.
- That was their biggest and last mistake.
- source_sentence: Do you feel sad or unhappy?
sentences:
- I have been depressed since late September so I feel you.
- I share a lot of your traits, and considered myself a failure too.
- He conveys that feeling of regret so well I can feel it everytime
- source_sentence: Do you feel hopeful about your future or do things seem hopeless?
sentences:
- I'm pretty optimistic though since the pace of technological growth is accelerating
so rapidly.
- '[For a clickable image, click here](http://futurism.com/thisweekinscience)
[To get these images directly to your inbox, sign up here](http://futurism.com/subscribe)
_
Sources | Reddit
--- | ---
[Oldest and Furthest Galaxy](http://futurism.com/links/astronomers-discover-the-oldest-and-farthest-known-galaxy/)
| [Reddit](https://www.reddit.com/r/science/comments/3jypyf/researchers_find_132_billion_yearold_galaxy_in/)
[3D Printed Ribs ](http://futurism.com/links/these-3d-printed-titanium-ribs-were-successfully-implanted-in-a-person/)
| [Reddit](https://www.reddit.com/r/technology/comments/3kj8pf/patient_receives_3dprinted_titanium_sternum_and/?ref=search_posts)
[Chinese Far Side of Moon] (http://m.phys.org/news/2015-09-china-aims-probe-moon-side.html) |
[Reddit](https://www.reddit.com/r/worldnews/comments/3kcsg5/china_to_explore_dark_side_of_the_moon_china_has/)
[Rugby Ball Molecule](http://www.forbes.com/sites/carmendrahl/2015/09/02/giant-rugby-ball-new-interaction-chemistry/)
| [Reddit](https://www.reddit.com/r/EverythingScience/comments/3krt22/this_giant_rugby_ball_contains_a_new_chemical/)
[Measuring the Universe](http://astronomynow.com/2015/09/04/using-stellar-twins-to-climb-the-cosmic-distance-ladder/)
| [Reddit](https://www.reddit.com/r/science/comments/3jum8c/astronomers_have_developed_a_new_highly_accurate/)
[3D Printed Stethoscope ](http://futurism.com/links/3d-printed-stethoscopes-cost-as-little-as-2-50-and-are-just-as-good/)
| [Reddit](https://www.reddit.com/r/news/comments/3kgboz/doctor_3d_prints_stethoscope_to_alleviate_supply/)
[Giant Structure in Universe](http://phys.org/news/2015-09-giant-ring-like-universe.html)
| [Reddit](https://www.reddit.com/r/EverythingScience/comments/3jzjlm/surprising_giant_ringlike_structure_in_the/)
[Recoded Cell Factories](http://m.phys.org/news/2015-09-recoded-cells-factories-proteins.html)
| [Reddit](https://www.reddit.com/r/EverythingScience/comments/3krux3/researchers_transform_recoded_cells_into/)'
- I do not expect things to work out for me.
- source_sentence: Do you feel sad or unhappy?
sentences:
- Me everyday im depressing
- And now I feel very alone and useless.
- Sucks that I'm not the only one because others are suffering, but it's nice to
know I'm not alone.
- source_sentence: Do you feel sad or unhappy?
sentences:
- I cried because I lost not only my money, but because I lost myself.
- Im not exactly depressed, at least not all of the time.
- does anyone feel like they cant be sad
pipeline_tag: sentence-similarity
library_name: sentence-transformers
---
# 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 e8c3b32edf5434bc2275fc9bab85f82640a19130 -->
- **Maximum Sequence Length:** 384 tokens
- **Output Dimensionality:** 768 dimensions
- **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, 'architecture': '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("FritzStack/mpnet_MH_embedding")
# Run inference
sentences = [
'Do you feel sad or unhappy?',
'Im not exactly depressed, at least not all of the time.',
'does anyone feel like they cant be sad',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[ 1.0000, 0.7532, -0.4572],
# [ 0.7532, 1.0000, -0.0545],
# [-0.4572, -0.0545, 1.0000]])
```
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### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
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## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 4,615 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: 9 tokens</li><li>mean: 13.63 tokens</li><li>max: 17 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 20.7 tokens</li><li>max: 169 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 42.11 tokens</li><li>max: 384 tokens</li></ul> |
* Samples:
| anchor | positive | negative |
|:-----------------------------------------|:------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>Do you feel sad or unhappy?</code> | <code>I do not feel sad.</code> | <code>I've been suffering my whole life, and it's currently at its peak :(</code> |
| <code>Do you feel sad or unhappy?</code> | <code>I feel sad much of the time.</code> | <code>Things will get better, just focus more in the positive rather than the negative</code> |
| <code>Do you feel sad or unhappy?</code> | <code>I am sad all the time.</code> | <code>That's why I understand I'm terrible, because it's wrong I get annoyed by that, people should do what they want, but I just can't stand being alone.</code> |
* Loss: [<code>TripletLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#tripletloss) with these parameters:
```json
{
"distance_metric": "TripletDistanceMetric.COSINE",
"triplet_margin": 0.5
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `per_device_train_batch_size`: 2
- `gradient_accumulation_steps`: 8
- `warmup_steps`: 100
- `fp16`: True
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: no
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 2
- `per_device_eval_batch_size`: 8
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 8
- `eval_accumulation_steps`: None
- `torch_empty_cache_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`: 3
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.0
- `warmup_steps`: 100
- `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
- `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}
- `parallelism_config`: None
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch_fused
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `project`: huggingface
- `trackio_space_id`: trackio
- `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`: None
- `hub_always_push`: False
- `hub_revision`: None
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `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
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: no
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `liger_kernel_config`: None
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: True
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional
- `router_mapping`: {}
- `learning_rate_mapping`: {}
</details>
### Training Logs
| Epoch | Step | Training Loss |
|:------:|:----:|:-------------:|
| 0.0347 | 10 | 0.3032 |
| 0.0693 | 20 | 0.2893 |
| 0.1040 | 30 | 0.2275 |
| 0.1386 | 40 | 0.1532 |
| 0.1733 | 50 | 0.1947 |
| 0.2080 | 60 | 0.1126 |
| 0.2426 | 70 | 0.1047 |
| 0.2773 | 80 | 0.1118 |
| 0.3120 | 90 | 0.0839 |
| 0.3466 | 100 | 0.1147 |
| 0.3813 | 110 | 0.111 |
| 0.4159 | 120 | 0.0754 |
| 0.4506 | 130 | 0.0964 |
| 0.4853 | 140 | 0.1269 |
| 0.5199 | 150 | 0.0795 |
| 0.5546 | 160 | 0.1042 |
| 0.5893 | 170 | 0.0797 |
| 0.6239 | 180 | 0.0685 |
| 0.6586 | 190 | 0.0819 |
| 0.6932 | 200 | 0.0802 |
| 0.7279 | 210 | 0.0934 |
| 0.7626 | 220 | 0.0865 |
| 0.7972 | 230 | 0.0731 |
| 0.8319 | 240 | 0.0486 |
| 0.8666 | 250 | 0.075 |
| 0.9012 | 260 | 0.0627 |
| 0.9359 | 270 | 0.0844 |
| 0.9705 | 280 | 0.0776 |
| 1.0035 | 290 | 0.0707 |
| 1.0381 | 300 | 0.0479 |
| 1.0728 | 310 | 0.05 |
| 1.1075 | 320 | 0.0317 |
| 1.1421 | 330 | 0.0263 |
| 1.1768 | 340 | 0.0321 |
| 1.2114 | 350 | 0.0221 |
| 1.2461 | 360 | 0.0337 |
| 1.2808 | 370 | 0.0301 |
| 1.3154 | 380 | 0.034 |
| 1.3501 | 390 | 0.0379 |
| 1.3847 | 400 | 0.0489 |
| 1.4194 | 410 | 0.0303 |
| 1.4541 | 420 | 0.0263 |
| 1.4887 | 430 | 0.0342 |
| 1.5234 | 440 | 0.0328 |
| 1.5581 | 450 | 0.0431 |
| 1.5927 | 460 | 0.0472 |
| 1.6274 | 470 | 0.0353 |
| 1.6620 | 480 | 0.0389 |
| 1.6967 | 490 | 0.0216 |
| 1.7314 | 500 | 0.0351 |
| 1.7660 | 510 | 0.0386 |
| 1.8007 | 520 | 0.039 |
| 1.8354 | 530 | 0.0264 |
| 1.8700 | 540 | 0.0295 |
| 1.9047 | 550 | 0.0329 |
| 1.9393 | 560 | 0.0487 |
| 1.9740 | 570 | 0.0287 |
| 2.0069 | 580 | 0.0306 |
| 2.0416 | 590 | 0.0171 |
| 2.0763 | 600 | 0.009 |
| 2.1109 | 610 | 0.017 |
| 2.1456 | 620 | 0.0252 |
| 2.1802 | 630 | 0.0123 |
| 2.2149 | 640 | 0.0144 |
| 2.2496 | 650 | 0.0187 |
| 2.2842 | 660 | 0.02 |
| 2.3189 | 670 | 0.0065 |
| 2.3536 | 680 | 0.0131 |
| 2.3882 | 690 | 0.0138 |
| 2.4229 | 700 | 0.0111 |
| 2.4575 | 710 | 0.0108 |
| 2.4922 | 720 | 0.0079 |
| 2.5269 | 730 | 0.0062 |
| 2.5615 | 740 | 0.0105 |
| 2.5962 | 750 | 0.0095 |
| 2.6308 | 760 | 0.0112 |
| 2.6655 | 770 | 0.0052 |
| 2.7002 | 780 | 0.0103 |
| 2.7348 | 790 | 0.0108 |
| 2.7695 | 800 | 0.0059 |
| 2.8042 | 810 | 0.0099 |
| 2.8388 | 820 | 0.0142 |
| 2.8735 | 830 | 0.0112 |
| 2.9081 | 840 | 0.0194 |
| 2.9428 | 850 | 0.0128 |
| 2.9775 | 860 | 0.0093 |
### Framework Versions
- Python: 3.12.12
- Sentence Transformers: 5.1.1
- Transformers: 4.57.1
- PyTorch: 2.8.0+cu126
- Accelerate: 1.10.1
- Datasets: 4.0.0
- Tokenizers: 0.22.1
## 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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