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---
language:
- en
library_name: sentence-transformers
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dataset_size:100K<n<1M
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
base_model: distilbert/distilroberta-base
metrics:
- pearson_cosine
- spearman_cosine
- pearson_manhattan
- spearman_manhattan
- pearson_euclidean
- spearman_euclidean
- pearson_dot
- spearman_dot
- pearson_max
- spearman_max
widget:
- source_sentence: Test Rocks
sentences:
- Number of testimonies
- People are at a pool.
- I've never been to Asia
- source_sentence: No animals.
sentences:
- We don't have a dog.
- These boys are on bikes
- A person is climbing.
- source_sentence: Shrinking.
sentences:
- That doesn't seem fair.
- A man reads the paper.
- I've never been to Asia
- source_sentence: Loire Valley
sentences:
- A Lake in Loire.
- people stand near pole
- A cat is licking itself.
- source_sentence: It is well.
sentences:
- That's convenient.
- away from the children
- She hated the restaurant!
pipeline_tag: sentence-similarity
model-index:
- name: SentenceTransformer based on distilbert/distilroberta-base
results:
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts dev 768
type: sts-dev-768
metrics:
- type: pearson_cosine
value: 0.8413274730706258
name: Pearson Cosine
- type: spearman_cosine
value: 0.8478057476815382
name: Spearman Cosine
- type: pearson_manhattan
value: 0.8414182910991368
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.8394684211369814
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.8423380151813549
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.8401129676358965
name: Spearman Euclidean
- type: pearson_dot
value: 0.7854982058734802
name: Pearson Dot
- type: spearman_dot
value: 0.7814388303641997
name: Spearman Dot
- type: pearson_max
value: 0.8423380151813549
name: Pearson Max
- type: spearman_max
value: 0.8478057476815382
name: Spearman Max
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts dev 512
type: sts-dev-512
metrics:
- type: pearson_cosine
value: 0.8394744649386727
name: Pearson Cosine
- type: spearman_cosine
value: 0.8469596264857904
name: Spearman Cosine
- type: pearson_manhattan
value: 0.8398552366754626
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.8377241640608183
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.8406514989809173
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.8380050330376462
name: Spearman Euclidean
- type: pearson_dot
value: 0.7811135781647157
name: Pearson Dot
- type: spearman_dot
value: 0.7776714775017128
name: Spearman Dot
- type: pearson_max
value: 0.8406514989809173
name: Pearson Max
- type: spearman_max
value: 0.8469596264857904
name: Spearman Max
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts dev 256
type: sts-dev-256
metrics:
- type: pearson_cosine
value: 0.8326846589795867
name: Pearson Cosine
- type: spearman_cosine
value: 0.8435757360139872
name: Spearman Cosine
- type: pearson_manhattan
value: 0.835121668379584
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.833167770567356
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.8359785864160201
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.8337674519096212
name: Spearman Euclidean
- type: pearson_dot
value: 0.7499541215721716
name: Pearson Dot
- type: spearman_dot
value: 0.7452815230357489
name: Spearman Dot
- type: pearson_max
value: 0.8359785864160201
name: Pearson Max
- type: spearman_max
value: 0.8435757360139872
name: Spearman Max
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts dev 128
type: sts-dev-128
metrics:
- type: pearson_cosine
value: 0.8243384464323462
name: Pearson Cosine
- type: spearman_cosine
value: 0.8399706247679909
name: Spearman Cosine
- type: pearson_manhattan
value: 0.8281897604718583
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.8270317815639731
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.8281918243965822
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.8267242273030063
name: Spearman Euclidean
- type: pearson_dot
value: 0.7110017325551932
name: Pearson Dot
- type: spearman_dot
value: 0.7049602384186016
name: Spearman Dot
- type: pearson_max
value: 0.8281918243965822
name: Pearson Max
- type: spearman_max
value: 0.8399706247679909
name: Spearman Max
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts dev 64
type: sts-dev-64
metrics:
- type: pearson_cosine
value: 0.811599959622093
name: Pearson Cosine
- type: spearman_cosine
value: 0.8316629408285197
name: Spearman Cosine
- type: pearson_manhattan
value: 0.8113103800424869
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.8104916438729426
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.8113924334973999
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.8110877753624469
name: Spearman Euclidean
- type: pearson_dot
value: 0.641225674602723
name: Pearson Dot
- type: spearman_dot
value: 0.6346995881423587
name: Spearman Dot
- type: pearson_max
value: 0.811599959622093
name: Pearson Max
- type: spearman_max
value: 0.8316629408285197
name: Spearman Max
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts dev 32
type: sts-dev-32
metrics:
- type: pearson_cosine
value: 0.7834130163353433
name: Pearson Cosine
- type: spearman_cosine
value: 0.814057381112976
name: Spearman Cosine
- type: pearson_manhattan
value: 0.7831854350286095
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.7859760066096324
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.7868628503474937
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.7893614397994021
name: Spearman Euclidean
- type: pearson_dot
value: 0.5533705216922039
name: Pearson Dot
- type: spearman_dot
value: 0.5449230360083127
name: Spearman Dot
- type: pearson_max
value: 0.7868628503474937
name: Pearson Max
- type: spearman_max
value: 0.814057381112976
name: Spearman Max
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts dev 16
type: sts-dev-16
metrics:
- type: pearson_cosine
value: 0.7259201534121641
name: Pearson Cosine
- type: spearman_cosine
value: 0.7751337117844075
name: Spearman Cosine
- type: pearson_manhattan
value: 0.7420762055565752
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.7552849049126117
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.7483211915991654
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.759888035465032
name: Spearman Euclidean
- type: pearson_dot
value: 0.4387404126202509
name: Pearson Dot
- type: spearman_dot
value: 0.42591442860202633
name: Spearman Dot
- type: pearson_max
value: 0.7483211915991654
name: Pearson Max
- type: spearman_max
value: 0.7751337117844075
name: Spearman Max
---
# SentenceTransformer based on distilbert/distilroberta-base
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [distilbert/distilroberta-base](https://huggingface.co/distilbert/distilroberta-base) on the [sentence-transformers/all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) dataset. 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:** [distilbert/distilroberta-base](https://huggingface.co/distilbert/distilroberta-base) <!-- at revision fb53ab8802853c8e4fbdbcd0529f21fc6f459b2b -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 tokens
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
- [sentence-transformers/all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli)
- **Language:** en
<!-- - **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: RobertaModel
(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("mrm8488/distilroberta-base-ft-allnli-matryoshka-768-16-1e-128bs")
# Run inference
sentences = [
'It is well.',
"That's convenient.",
'away from the children',
]
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.*
-->
## Evaluation
### Metrics
#### Semantic Similarity
* Dataset: `sts-dev-768`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.8413 |
| **spearman_cosine** | **0.8478** |
| pearson_manhattan | 0.8414 |
| spearman_manhattan | 0.8395 |
| pearson_euclidean | 0.8423 |
| spearman_euclidean | 0.8401 |
| pearson_dot | 0.7855 |
| spearman_dot | 0.7814 |
| pearson_max | 0.8423 |
| spearman_max | 0.8478 |
#### Semantic Similarity
* Dataset: `sts-dev-512`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:----------|
| pearson_cosine | 0.8395 |
| **spearman_cosine** | **0.847** |
| pearson_manhattan | 0.8399 |
| spearman_manhattan | 0.8377 |
| pearson_euclidean | 0.8407 |
| spearman_euclidean | 0.838 |
| pearson_dot | 0.7811 |
| spearman_dot | 0.7777 |
| pearson_max | 0.8407 |
| spearman_max | 0.847 |
#### Semantic Similarity
* Dataset: `sts-dev-256`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.8327 |
| **spearman_cosine** | **0.8436** |
| pearson_manhattan | 0.8351 |
| spearman_manhattan | 0.8332 |
| pearson_euclidean | 0.836 |
| spearman_euclidean | 0.8338 |
| pearson_dot | 0.75 |
| spearman_dot | 0.7453 |
| pearson_max | 0.836 |
| spearman_max | 0.8436 |
#### Semantic Similarity
* Dataset: `sts-dev-128`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:---------|
| pearson_cosine | 0.8243 |
| **spearman_cosine** | **0.84** |
| pearson_manhattan | 0.8282 |
| spearman_manhattan | 0.827 |
| pearson_euclidean | 0.8282 |
| spearman_euclidean | 0.8267 |
| pearson_dot | 0.711 |
| spearman_dot | 0.705 |
| pearson_max | 0.8282 |
| spearman_max | 0.84 |
#### Semantic Similarity
* Dataset: `sts-dev-64`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.8116 |
| **spearman_cosine** | **0.8317** |
| pearson_manhattan | 0.8113 |
| spearman_manhattan | 0.8105 |
| pearson_euclidean | 0.8114 |
| spearman_euclidean | 0.8111 |
| pearson_dot | 0.6412 |
| spearman_dot | 0.6347 |
| pearson_max | 0.8116 |
| spearman_max | 0.8317 |
#### Semantic Similarity
* Dataset: `sts-dev-32`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.7834 |
| **spearman_cosine** | **0.8141** |
| pearson_manhattan | 0.7832 |
| spearman_manhattan | 0.786 |
| pearson_euclidean | 0.7869 |
| spearman_euclidean | 0.7894 |
| pearson_dot | 0.5534 |
| spearman_dot | 0.5449 |
| pearson_max | 0.7869 |
| spearman_max | 0.8141 |
#### Semantic Similarity
* Dataset: `sts-dev-16`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.7259 |
| **spearman_cosine** | **0.7751** |
| pearson_manhattan | 0.7421 |
| spearman_manhattan | 0.7553 |
| pearson_euclidean | 0.7483 |
| spearman_euclidean | 0.7599 |
| pearson_dot | 0.4387 |
| spearman_dot | 0.4259 |
| pearson_max | 0.7483 |
| spearman_max | 0.7751 |
<!--
## 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
#### sentence-transformers/all-nli
* Dataset: [sentence-transformers/all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) at [d482672](https://huggingface.co/datasets/sentence-transformers/all-nli/tree/d482672c8e74ce18da116f430137434ba2e52fab)
* Size: 557,850 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: 10.38 tokens</li><li>max: 45 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 12.8 tokens</li><li>max: 39 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 13.4 tokens</li><li>max: 50 tokens</li></ul> |
* Samples:
| anchor | positive | negative |
|:---------------------------------------------------------------------------|:-------------------------------------------------|:-----------------------------------------------------------|
| <code>A person on a horse jumps over a broken down airplane.</code> | <code>A person is outdoors, on a horse.</code> | <code>A person is at a diner, ordering an omelette.</code> |
| <code>Children smiling and waving at camera</code> | <code>There are children present</code> | <code>The kids are frowning</code> |
| <code>A boy is jumping on skateboard in the middle of a red bridge.</code> | <code>The boy does a skateboarding trick.</code> | <code>The boy skates down the sidewalk.</code> |
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
```json
{
"loss": "MultipleNegativesRankingLoss",
"matryoshka_dims": [
768,
512,
256,
128,
64,
32,
16
],
"matryoshka_weights": [
1,
1,
1,
1,
1,
1,
1
],
"n_dims_per_step": -1
}
```
### Evaluation Dataset
#### sentence-transformers/all-nli
* Dataset: [sentence-transformers/all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) at [d482672](https://huggingface.co/datasets/sentence-transformers/all-nli/tree/d482672c8e74ce18da116f430137434ba2e52fab)
* Size: 6,584 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: 6 tokens</li><li>mean: 18.02 tokens</li><li>max: 66 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 9.81 tokens</li><li>max: 29 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 10.37 tokens</li><li>max: 29 tokens</li></ul> |
* Samples:
| anchor | positive | negative |
|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------|:--------------------------------------------------------|
| <code>Two women are embracing while holding to go packages.</code> | <code>Two woman are holding packages.</code> | <code>The men are fighting outside a deli.</code> |
| <code>Two young children in blue jerseys, one with the number 9 and one with the number 2 are standing on wooden steps in a bathroom and washing their hands in a sink.</code> | <code>Two kids in numbered jerseys wash their hands.</code> | <code>Two kids in jackets walk to school.</code> |
| <code>A man selling donuts to a customer during a world exhibition event held in the city of Angeles</code> | <code>A man selling donuts to a customer.</code> | <code>A woman drinks her coffee in a small cafe.</code> |
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
```json
{
"loss": "MultipleNegativesRankingLoss",
"matryoshka_dims": [
768,
512,
256,
128,
64,
32,
16
],
"matryoshka_weights": [
1,
1,
1,
1,
1,
1,
1
],
"n_dims_per_step": -1
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 128
- `per_device_eval_batch_size`: 128
- `num_train_epochs`: 1
- `warmup_ratio`: 0.1
- `fp16`: True
- `batch_sampler`: no_duplicates
#### 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`: 128
- `per_device_eval_batch_size`: 128
- `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`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
| Epoch | Step | Training Loss | loss | sts-dev-128_spearman_cosine | sts-dev-16_spearman_cosine | sts-dev-256_spearman_cosine | sts-dev-32_spearman_cosine | sts-dev-512_spearman_cosine | sts-dev-64_spearman_cosine | sts-dev-768_spearman_cosine |
|:------:|:----:|:-------------:|:-------:|:---------------------------:|:--------------------------:|:---------------------------:|:--------------------------:|:---------------------------:|:--------------------------:|:---------------------------:|
| 0.0229 | 100 | 29.0917 | 14.1514 | 0.7659 | 0.7440 | 0.7915 | 0.7749 | 0.7999 | 0.7909 | 0.7918 |
| 0.0459 | 200 | 15.6915 | 11.7031 | 0.7718 | 0.7487 | 0.7940 | 0.7776 | 0.8005 | 0.7931 | 0.7871 |
| 0.0688 | 300 | 14.3136 | 11.1970 | 0.7744 | 0.7389 | 0.7952 | 0.7728 | 0.8036 | 0.7925 | 0.7938 |
| 0.0918 | 400 | 12.8122 | 10.4416 | 0.7899 | 0.7536 | 0.8040 | 0.7764 | 0.8065 | 0.7953 | 0.8018 |
| 0.1147 | 500 | 12.1747 | 10.5491 | 0.7871 | 0.7513 | 0.8035 | 0.7785 | 0.8094 | 0.7978 | 0.8008 |
| 0.1376 | 600 | 11.6784 | 9.6618 | 0.7785 | 0.7465 | 0.7956 | 0.7762 | 0.8027 | 0.7953 | 0.7935 |
| 0.1606 | 700 | 11.9351 | 9.3279 | 0.7907 | 0.7403 | 0.7995 | 0.7706 | 0.8036 | 0.7894 | 0.7982 |
| 0.1835 | 800 | 10.4998 | 9.1538 | 0.7911 | 0.7516 | 0.8043 | 0.7820 | 0.8078 | 0.8025 | 0.8010 |
| 0.2065 | 900 | 10.6069 | 9.0531 | 0.7874 | 0.7371 | 0.7974 | 0.7704 | 0.8042 | 0.7910 | 0.8010 |
| 0.2294 | 1000 | 10.0316 | 8.9759 | 0.7842 | 0.7356 | 0.7981 | 0.7721 | 0.8024 | 0.7905 | 0.7955 |
| 0.2524 | 1100 | 10.199 | 8.5398 | 0.7863 | 0.7322 | 0.7961 | 0.7691 | 0.8002 | 0.7910 | 0.7936 |
| 0.2753 | 1200 | 9.9393 | 8.1356 | 0.7860 | 0.7304 | 0.7990 | 0.7682 | 0.8025 | 0.7908 | 0.7954 |
| 0.2982 | 1300 | 9.8711 | 7.9177 | 0.7932 | 0.7319 | 0.8028 | 0.7708 | 0.8067 | 0.7924 | 0.8013 |
| 0.3212 | 1400 | 9.3594 | 7.8870 | 0.7892 | 0.7296 | 0.8032 | 0.7710 | 0.8070 | 0.7961 | 0.8030 |
| 0.3441 | 1500 | 9.4534 | 7.5756 | 0.8003 | 0.7518 | 0.8078 | 0.7857 | 0.8112 | 0.8063 | 0.8068 |
| 0.3671 | 1600 | 8.9061 | 7.8164 | 0.7781 | 0.7390 | 0.7942 | 0.7761 | 0.8002 | 0.7968 | 0.7941 |
| 0.3900 | 1700 | 8.5164 | 7.4869 | 0.7934 | 0.7530 | 0.8063 | 0.7864 | 0.8120 | 0.8055 | 0.8080 |
| 0.4129 | 1800 | 8.9262 | 7.7155 | 0.7846 | 0.7301 | 0.7991 | 0.7728 | 0.8065 | 0.7945 | 0.8003 |
| 0.4359 | 1900 | 8.3242 | 7.3068 | 0.7850 | 0.7273 | 0.7976 | 0.7710 | 0.8020 | 0.7904 | 0.7976 |
| 0.4588 | 2000 | 8.5374 | 7.1026 | 0.7845 | 0.7272 | 0.7993 | 0.7717 | 0.8042 | 0.7925 | 0.7963 |
| 0.4818 | 2100 | 8.2304 | 7.1601 | 0.7879 | 0.7354 | 0.8015 | 0.7719 | 0.8059 | 0.7944 | 0.8029 |
| 0.5047 | 2200 | 8.1347 | 7.8267 | 0.7715 | 0.7230 | 0.7889 | 0.7626 | 0.7956 | 0.7849 | 0.7930 |
| 0.5276 | 2300 | 8.3057 | 8.0057 | 0.7622 | 0.7148 | 0.7814 | 0.7572 | 0.7881 | 0.7769 | 0.7836 |
| 0.5506 | 2400 | 8.215 | 7.6922 | 0.7772 | 0.7210 | 0.7929 | 0.7637 | 0.7995 | 0.7858 | 0.7956 |
| 0.5735 | 2500 | 8.4343 | 7.2104 | 0.7869 | 0.7307 | 0.8017 | 0.7707 | 0.8071 | 0.7929 | 0.8048 |
| 0.5965 | 2600 | 8.159 | 6.9977 | 0.7893 | 0.7297 | 0.8031 | 0.7733 | 0.8071 | 0.7928 | 0.8045 |
| 0.6194 | 2700 | 8.2048 | 6.9465 | 0.7859 | 0.7280 | 0.8006 | 0.7725 | 0.8052 | 0.7926 | 0.8004 |
| 0.6423 | 2800 | 8.187 | 7.3185 | 0.7790 | 0.7266 | 0.7960 | 0.7690 | 0.8018 | 0.7911 | 0.7964 |
| 0.6653 | 2900 | 8.4768 | 7.5535 | 0.7756 | 0.7192 | 0.7913 | 0.7618 | 0.7958 | 0.7827 | 0.7907 |
| 0.6882 | 3000 | 8.4153 | 7.3732 | 0.7825 | 0.7276 | 0.7988 | 0.7692 | 0.8029 | 0.7899 | 0.7988 |
| 0.7112 | 3100 | 7.9226 | 6.8469 | 0.7912 | 0.7311 | 0.8055 | 0.7765 | 0.8101 | 0.7977 | 0.8058 |
| 0.7341 | 3200 | 8.1155 | 6.7604 | 0.7880 | 0.7298 | 0.8024 | 0.7747 | 0.8071 | 0.7959 | 0.8025 |
| 0.7571 | 3300 | 6.8463 | 5.4863 | 0.8357 | 0.7638 | 0.8407 | 0.8085 | 0.8431 | 0.8283 | 0.8440 |
| 0.7800 | 3400 | 5.2008 | 5.2472 | 0.8362 | 0.7655 | 0.8401 | 0.8105 | 0.8429 | 0.8279 | 0.8445 |
| 0.8029 | 3500 | 4.5415 | 5.1649 | 0.8385 | 0.7700 | 0.8421 | 0.8138 | 0.8454 | 0.8304 | 0.8465 |
| 0.8259 | 3600 | 4.4474 | 5.0933 | 0.8371 | 0.7693 | 0.8410 | 0.8112 | 0.8443 | 0.8288 | 0.8451 |
| 0.8488 | 3700 | 4.12 | 5.0555 | 0.8396 | 0.7718 | 0.8439 | 0.8140 | 0.8463 | 0.8311 | 0.8471 |
| 0.8718 | 3800 | 3.9104 | 5.0147 | 0.8386 | 0.7749 | 0.8432 | 0.8129 | 0.8459 | 0.8304 | 0.8471 |
| 0.8947 | 3900 | 3.9054 | 4.9966 | 0.8379 | 0.7733 | 0.8424 | 0.8125 | 0.8456 | 0.8296 | 0.8464 |
| 0.9176 | 4000 | 3.757 | 4.9892 | 0.8407 | 0.7763 | 0.8447 | 0.8156 | 0.8478 | 0.8326 | 0.8488 |
| 0.9406 | 4100 | 3.7729 | 4.9859 | 0.8400 | 0.7751 | 0.8436 | 0.8141 | 0.8470 | 0.8317 | 0.8478 |
### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.0.0
- Transformers: 4.41.1
- PyTorch: 2.3.0+cu121
- Accelerate: 0.30.1
- 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",
}
```
#### MatryoshkaLoss
```bibtex
@misc{kusupati2024matryoshka,
title={Matryoshka Representation Learning},
author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
year={2024},
eprint={2205.13147},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
```
#### MultipleNegativesRankingLoss
```bibtex
@misc{henderson2017efficient,
title={Efficient Natural Language Response Suggestion for Smart Reply},
author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
year={2017},
eprint={1705.00652},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
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