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---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:2351
- loss:CosineSimilarityLoss
base_model: sentence-transformers/all-mpnet-base-v2
widget:
- source_sentence: Are your thoughts sometimes so strong that you can almost hear
them?
sentences:
- My emotions have almost always seemed flat regardless of what is going on around
me.
- Having powerful images or memories that sometimes come into your mind in which
you feel the experience is happening again in the here and now?
- I often think that I hear people talking only to discover that there was no one
there.
- source_sentence: Having difficulty concentrating?
sentences:
- My thoughts are so hazy and unclear that I wish that I could just reach up and
put them into place.
- Most of the time I find it is very difficult to get my thoughts in order.
- Experienced sleep disturbances?
- source_sentence: Feeling jumpy or easily startled?
sentences:
- I often worry that someone or something is controlling my behavior.
- People find my conversations to be confusing or hard to follow.
- Worried a lot about different things?
- source_sentence: Do you often have to keep an eye out to stop people from taking
advantage of you?
sentences:
- I find that I am very often confused about what is going on around me.
- I sometimes wonder if there is a small group of people who can control everyone
else's behavior.
- I have sometimes felt that strangers were reading my mind.
- source_sentence: I am not good at expressing my true feelings by the way I talk
and look.
sentences:
- Felt down or depressed for most of the day
- Felt nervous or anxious?
- Experienced sleep disturbances?
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- pearson_cosine
- spearman_cosine
model-index:
- name: SentenceTransformer based on sentence-transformers/all-mpnet-base-v2
results:
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: Unknown
type: unknown
metrics:
- type: pearson_cosine
value: 0.5680489773046146
name: Pearson Cosine
- type: spearman_cosine
value: 0.5532689999140259
name: Spearman Cosine
---
# 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 9a3225965996d404b775526de6dbfe85d3368642 -->
- **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}) 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("sentence_transformers_model_id")
# Run inference
sentences = [
'I am not good at expressing my true feelings by the way I talk and look.',
'Felt nervous or anxious?',
'Experienced sleep disturbances?',
]
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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<details><summary>Click to expand</summary>
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## Evaluation
### Metrics
#### Semantic Similarity
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.568 |
| **spearman_cosine** | **0.5533** |
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## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 2,351 training samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | score |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
| type | string | string | float |
| details | <ul><li>min: 5 tokens</li><li>mean: 16.73 tokens</li><li>max: 47 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 14.82 tokens</li><li>max: 28 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.26</li><li>max: 1.0</li></ul> |
* Samples:
| sentence1 | sentence2 | score |
|:--------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------|:------------------|
| <code>Do you believe in telepathy (mind-reading)?</code> | <code>I believe that there are secret signs in the world if you just know how to look for them.</code> | <code>0.15</code> |
| <code>Irritable behavior, angry outbursts, or acting aggressively?</code> | <code>Felt “on edge”?</code> | <code>0.62</code> |
| <code>I have some eccentric (odd) habits.</code> | <code>I often have difficulty following what someone is saying to me.</code> | <code>0.0</code> |
* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
```json
{
"loss_fct": "torch.nn.modules.loss.L1Loss"
}
```
### Evaluation Dataset
#### Unnamed Dataset
* Size: 236 evaluation samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
* Approximate statistics based on the first 236 samples:
| | sentence1 | sentence2 | score |
|:--------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
| type | string | string | float |
| details | <ul><li>min: 6 tokens</li><li>mean: 16.4 tokens</li><li>max: 47 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 14.76 tokens</li><li>max: 28 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.29</li><li>max: 1.0</li></ul> |
* Samples:
| sentence1 | sentence2 | score |
|:----------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------|:------------------|
| <code>Feeling afraid as if something awful might happen?</code> | <code>I have trouble following conversations with others.</code> | <code>0.19</code> |
| <code>Do you believe in telepathy (mind-reading)?</code> | <code>Feeling jumpy or easily startled?</code> | <code>0.1</code> |
| <code>Other people see me as slightly eccentric (odd).</code> | <code>I have felt that there were messages for me in the way things were arranged, like furniture in a room.</code> | <code>0.0</code> |
* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
```json
{
"loss_fct": "torch.nn.modules.loss.L1Loss"
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 16
#### 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`: 8
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `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`: 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`: None
- `hub_always_push`: False
- `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
- `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
- `eval_on_start`: False
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
| Epoch | Step | Training Loss | Validation Loss | spearman_cosine |
|:------:|:----:|:-------------:|:---------------:|:---------------:|
| 0.0680 | 10 | 0.2239 | - | - |
| 0.1361 | 20 | 0.2188 | - | - |
| 0.2041 | 30 | 0.2007 | - | - |
| 0.2721 | 40 | 0.2045 | - | - |
| 0.3401 | 50 | 0.2179 | 0.2197 | - |
| 0.4082 | 60 | 0.2106 | - | - |
| 0.4762 | 70 | 0.2124 | - | - |
| 0.5442 | 80 | 0.2046 | - | - |
| 0.6122 | 90 | 0.2069 | - | - |
| 0.6803 | 100 | 0.1965 | 0.2112 | - |
| 0.7483 | 110 | 0.2355 | - | - |
| 0.8163 | 120 | 0.2012 | - | - |
| 0.8844 | 130 | 0.2402 | - | - |
| 0.9524 | 140 | 0.2173 | - | - |
| 1.0204 | 150 | 0.1763 | 0.2043 | - |
| 1.0884 | 160 | 0.1862 | - | - |
| 1.1565 | 170 | 0.1854 | - | - |
| 1.2245 | 180 | 0.193 | - | - |
| 1.2925 | 190 | 0.1852 | - | - |
| 1.3605 | 200 | 0.1908 | 0.1950 | - |
| 1.4286 | 210 | 0.2002 | - | - |
| 1.4966 | 220 | 0.1945 | - | - |
| 1.5646 | 230 | 0.193 | - | - |
| 1.6327 | 240 | 0.1893 | - | - |
| 1.7007 | 250 | 0.171 | 0.1937 | - |
| 1.7687 | 260 | 0.1848 | - | - |
| 1.8367 | 270 | 0.1909 | - | - |
| 1.9048 | 280 | 0.2138 | - | - |
| 1.9728 | 290 | 0.2014 | - | - |
| 2.0408 | 300 | 0.1855 | 0.1867 | - |
| 2.1088 | 310 | 0.1891 | - | - |
| 2.1769 | 320 | 0.1849 | - | - |
| 2.2449 | 330 | 0.1741 | - | - |
| 2.3129 | 340 | 0.1775 | - | - |
| 2.3810 | 350 | 0.178 | 0.1871 | - |
| 2.4490 | 360 | 0.1778 | - | - |
| 2.5170 | 370 | 0.174 | - | - |
| 2.5850 | 380 | 0.1654 | - | - |
| 2.6531 | 390 | 0.1954 | - | - |
| 2.7211 | 400 | 0.1584 | 0.1860 | - |
| 2.7891 | 410 | 0.2019 | - | - |
| 2.8571 | 420 | 0.1941 | - | - |
| 2.9252 | 430 | 0.1855 | - | - |
| 2.9932 | 440 | 0.1823 | - | - |
| 3.0 | 441 | - | - | 0.5533 |
### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.3.1
- Transformers: 4.47.1
- PyTorch: 2.5.1+cu121
- Accelerate: 1.2.1
- Datasets: 3.2.0
- Tokenizers: 0.21.0
## 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",
}
```
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