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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: He shrugged.
sentences:
- Then he shrugged.
- Two people are dancing.
- The people are Indian.
- source_sentence: a young girl
sentences:
- A girl is playing.
- A dog playing outside.
- The men are moving.
- source_sentence: girl sleeps
sentences:
- A little girl is sleep.
- Two women are walking.
- three men are pictured
- source_sentence: He walked.
sentences:
- A man is moving around.
- A young man is running.
- What idiots girls are!
- source_sentence: '''Go now.'''
sentences:
- Now go.
- The door did not budge.
- I never knew the man.
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: 0.8418367310465795
name: Pearson Cosine
- type: spearman_cosine
value: 0.8485984004433933
name: Spearman Cosine
- type: pearson_manhattan
value: 0.8356556933767024
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.8341402433895243
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.8378021883964464
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.8364904078404392
name: Spearman Euclidean
- type: pearson_dot
value: 0.7476524989991268
name: Pearson Dot
- type: spearman_dot
value: 0.744450587024694
name: Spearman Dot
- type: pearson_max
value: 0.8418367310465795
name: Pearson Max
- type: spearman_max
value: 0.8485984004433933
name: Spearman Max
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts dev 512
type: sts-dev-512
metrics:
- type: pearson_cosine
value: 0.8416891989714739
name: Pearson Cosine
- type: spearman_cosine
value: 0.8490082509626217
name: Spearman Cosine
- type: pearson_manhattan
value: 0.8348187780435371
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.8332638443518806
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.837008948364763
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.8356608810942396
name: Spearman Euclidean
- type: pearson_dot
value: 0.7426437744526075
name: Pearson Dot
- type: spearman_dot
value: 0.7393063147821313
name: Spearman Dot
- type: pearson_max
value: 0.8416891989714739
name: Pearson Max
- type: spearman_max
value: 0.8490082509626217
name: Spearman Max
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts dev 256
type: sts-dev-256
metrics:
- type: pearson_cosine
value: 0.8368212220308662
name: Pearson Cosine
- type: spearman_cosine
value: 0.8458532859579723
name: Spearman Cosine
- type: pearson_manhattan
value: 0.8282949195581827
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.8279757292284411
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.8304309516656533
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.8301347336633305
name: Spearman Euclidean
- type: pearson_dot
value: 0.7158283880571648
name: Pearson Dot
- type: spearman_dot
value: 0.7114038350641958
name: Spearman Dot
- type: pearson_max
value: 0.8368212220308662
name: Pearson Max
- type: spearman_max
value: 0.8458532859579723
name: Spearman Max
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts dev 128
type: sts-dev-128
metrics:
- type: pearson_cosine
value: 0.8291552182220155
name: Pearson Cosine
- type: spearman_cosine
value: 0.8410315378567165
name: Spearman Cosine
- type: pearson_manhattan
value: 0.8205197124842151
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.8211956528048456
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.8218377581296912
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.8223376697977559
name: Spearman Euclidean
- type: pearson_dot
value: 0.6736747525126793
name: Pearson Dot
- type: spearman_dot
value: 0.6704632728499174
name: Spearman Dot
- type: pearson_max
value: 0.8291552182220155
name: Pearson Max
- type: spearman_max
value: 0.8410315378567165
name: Spearman Max
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts dev 64
type: sts-dev-64
metrics:
- type: pearson_cosine
value: 0.8201110050860942
name: Pearson Cosine
- type: spearman_cosine
value: 0.835036509147006
name: Spearman Cosine
- type: pearson_manhattan
value: 0.8028297556674707
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.8048509047037822
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.8046682420071583
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.8063788129340022
name: Spearman Euclidean
- type: pearson_dot
value: 0.6171580093307325
name: Pearson Dot
- type: spearman_dot
value: 0.6176751811391049
name: Spearman Dot
- type: pearson_max
value: 0.8201110050860942
name: Pearson Max
- type: spearman_max
value: 0.835036509147006
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("sentence_transformers_model_id")
# Run inference
sentences = [
"'Go now.'",
'Now go.',
'The door did not budge.',
]
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 | 0.8418 |
| **spearman_cosine** | **0.8486** |
| pearson_manhattan | 0.8357 |
| spearman_manhattan | 0.8341 |
| pearson_euclidean | 0.8378 |
| spearman_euclidean | 0.8365 |
| pearson_dot | 0.7477 |
| spearman_dot | 0.7445 |
| pearson_max | 0.8418 |
| spearman_max | 0.8486 |
#### 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 | 0.8417 |
| **spearman_cosine** | **0.849** |
| pearson_manhattan | 0.8348 |
| spearman_manhattan | 0.8333 |
| pearson_euclidean | 0.837 |
| spearman_euclidean | 0.8357 |
| pearson_dot | 0.7426 |
| spearman_dot | 0.7393 |
| pearson_max | 0.8417 |
| spearman_max | 0.849 |
#### 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 | 0.8368 |
| **spearman_cosine** | **0.8459** |
| pearson_manhattan | 0.8283 |
| spearman_manhattan | 0.828 |
| pearson_euclidean | 0.8304 |
| spearman_euclidean | 0.8301 |
| pearson_dot | 0.7158 |
| spearman_dot | 0.7114 |
| pearson_max | 0.8368 |
| spearman_max | 0.8459 |
#### 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 | 0.8292 |
| **spearman_cosine** | **0.841** |
| pearson_manhattan | 0.8205 |
| spearman_manhattan | 0.8212 |
| pearson_euclidean | 0.8218 |
| spearman_euclidean | 0.8223 |
| pearson_dot | 0.6737 |
| spearman_dot | 0.6705 |
| pearson_max | 0.8292 |
| spearman_max | 0.841 |
#### 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 | 0.8201 |
| **spearman_cosine** | **0.835** |
| pearson_manhattan | 0.8028 |
| spearman_manhattan | 0.8049 |
| pearson_euclidean | 0.8047 |
| spearman_euclidean | 0.8064 |
| pearson_dot | 0.6172 |
| spearman_dot | 0.6177 |
| pearson_max | 0.8201 |
| spearman_max | 0.835 |
<!--
## 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
],
"matryoshka_weights": [
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
],
"matryoshka_weights": [
1,
1,
1,
1,
1
],
"n_dims_per_step": -1
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 256
- `per_device_eval_batch_size`: 256
- `num_train_epochs`: 1
- `warmup_ratio`: 0.1
- `bf16`: 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`: 256
- `per_device_eval_batch_size`: 256
- `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`: True
- `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`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
| Epoch | Step | Training Loss | loss | sts-dev-128_spearman_cosine | sts-dev-256_spearman_cosine | sts-dev-512_spearman_cosine | sts-dev-64_spearman_cosine | sts-dev-768_spearman_cosine |
|:------:|:----:|:-------------:|:------:|:---------------------------:|:---------------------------:|:---------------------------:|:--------------------------:|:---------------------------:|
| 0.0459 | 100 | 19.459 | 8.2665 | 0.7796 | 0.8046 | 0.8114 | 0.8082 | 0.7996 |
| 0.0917 | 200 | 11.0035 | 7.6606 | 0.7696 | 0.7971 | 0.8083 | 0.7987 | 0.7933 |
| 0.1376 | 300 | 9.7634 | 6.4912 | 0.7992 | 0.8126 | 0.8190 | 0.8062 | 0.8127 |
| 0.1835 | 400 | 9.1103 | 5.9960 | 0.8081 | 0.8229 | 0.8263 | 0.8136 | 0.8224 |
| 0.2294 | 500 | 8.7099 | 5.9388 | 0.7984 | 0.8138 | 0.8189 | 0.8021 | 0.8166 |
| 0.2752 | 600 | 8.1215 | 5.6457 | 0.7963 | 0.8104 | 0.8149 | 0.8057 | 0.8121 |
| 0.3211 | 700 | 7.7441 | 5.4632 | 0.7937 | 0.8153 | 0.8199 | 0.8119 | 0.8150 |
| 0.3670 | 800 | 7.4849 | 5.1815 | 0.8076 | 0.8208 | 0.8238 | 0.8152 | 0.8172 |
| 0.4128 | 900 | 7.1386 | 5.1419 | 0.8035 | 0.8181 | 0.8235 | 0.8139 | 0.8189 |
| 0.4587 | 1000 | 6.839 | 5.1548 | 0.7943 | 0.8118 | 0.8172 | 0.8054 | 0.8153 |
| 0.5046 | 1100 | 6.6597 | 5.1015 | 0.7895 | 0.8066 | 0.8119 | 0.8059 | 0.8063 |
| 0.5505 | 1200 | 6.7172 | 5.3707 | 0.7753 | 0.7987 | 0.8068 | 0.7989 | 0.8014 |
| 0.5963 | 1300 | 6.6514 | 4.9368 | 0.7904 | 0.8086 | 0.8139 | 0.8051 | 0.8083 |
| 0.6422 | 1400 | 6.5573 | 5.0196 | 0.7882 | 0.8066 | 0.8128 | 0.8035 | 0.8091 |
| 0.6881 | 1500 | 6.7596 | 4.9381 | 0.7960 | 0.8120 | 0.8169 | 0.8058 | 0.8140 |
| 0.7339 | 1600 | 6.2686 | 4.4018 | 0.8136 | 0.8245 | 0.8268 | 0.8160 | 0.8244 |
| 0.7798 | 1700 | 3.4607 | 3.8397 | 0.8415 | 0.8466 | 0.8502 | 0.8345 | 0.8503 |
| 0.8257 | 1800 | 2.6912 | 3.7914 | 0.8415 | 0.8459 | 0.8493 | 0.8350 | 0.8488 |
| 0.8716 | 1900 | 2.4958 | 3.7752 | 0.8402 | 0.8450 | 0.8484 | 0.8340 | 0.8478 |
| 0.9174 | 2000 | 2.3413 | 3.7997 | 0.8410 | 0.8459 | 0.8490 | 0.8350 | 0.8486 |
### 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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