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---
language:
- en
library_name: sentence-transformers
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dataset_size:10K<n<100K
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
base_model: l3cube-pune/indic-sentence-similarity-sbert
metrics:
- pearson_cosine
- spearman_cosine
- pearson_manhattan
- spearman_manhattan
- pearson_euclidean
- spearman_euclidean
- pearson_dot
- spearman_dot
- pearson_max
- spearman_max
widget:
- source_sentence: Excuse me.
sentences:
- um pardon me
- A man is opening mail.
- The girls are indoors.
- source_sentence: Double pig.
sentences:
- Ah, triple pig!
- a girl poses for camera
- Girls dance together.
- source_sentence: People pose.
sentences:
- People are smiling.
- I know a few old ones.
- The boy fell off his bike.
- source_sentence: A man sings.
sentences:
- People singing
- A man is playing golf.
- The women are eating bread.
- source_sentence: Then he ran.
sentences:
- He then started to run.
- A man plays the flute.
- A couple sit on the couch
pipeline_tag: sentence-similarity
model-index:
- name: SentenceTransformer based on l3cube-pune/indic-sentence-similarity-sbert
results:
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts dev 768
type: sts-dev-768
metrics:
- type: pearson_cosine
value: 0.8608857207512975
name: Pearson Cosine
- type: spearman_cosine
value: 0.8662860178080238
name: Spearman Cosine
- type: pearson_manhattan
value: 0.858692209351004
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.8612472945208892
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.858472048314985
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.8611276457994067
name: Spearman Euclidean
- type: pearson_dot
value: 0.8258747949887901
name: Pearson Dot
- type: spearman_dot
value: 0.8259736371824636
name: Spearman Dot
- type: pearson_max
value: 0.8608857207512975
name: Pearson Max
- type: spearman_max
value: 0.8662860178080238
name: Spearman Max
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts dev 512
type: sts-dev-512
metrics:
- type: pearson_cosine
value: 0.8594405198312016
name: Pearson Cosine
- type: spearman_cosine
value: 0.8648571300070264
name: Spearman Cosine
- type: pearson_manhattan
value: 0.8574291650964095
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.8598780673781499
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.8574540367546871
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.8600722932569861
name: Spearman Euclidean
- type: pearson_dot
value: 0.822340474813523
name: Pearson Dot
- type: spearman_dot
value: 0.8226609928783558
name: Spearman Dot
- type: pearson_max
value: 0.8594405198312016
name: Pearson Max
- type: spearman_max
value: 0.8648571300070264
name: Spearman Max
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts dev 256
type: sts-dev-256
metrics:
- type: pearson_cosine
value: 0.8506120561071212
name: Pearson Cosine
- type: spearman_cosine
value: 0.8575982860981437
name: Spearman Cosine
- type: pearson_manhattan
value: 0.852829777566948
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.8552667517015687
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.8526934293405145
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.8551077930316164
name: Spearman Euclidean
- type: pearson_dot
value: 0.7943956137623474
name: Pearson Dot
- type: spearman_dot
value: 0.7963976287579885
name: Spearman Dot
- type: pearson_max
value: 0.852829777566948
name: Pearson Max
- type: spearman_max
value: 0.8575982860981437
name: Spearman Max
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts dev 128
type: sts-dev-128
metrics:
- type: pearson_cosine
value: 0.8410977354989039
name: Pearson Cosine
- type: spearman_cosine
value: 0.850480817077266
name: Spearman Cosine
- type: pearson_manhattan
value: 0.8461619224798919
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.8490393633313068
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.8458138708136093
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.848719989437845
name: Spearman Euclidean
- type: pearson_dot
value: 0.7755878071062363
name: Pearson Dot
- type: spearman_dot
value: 0.7755629190322909
name: Spearman Dot
- type: pearson_max
value: 0.8461619224798919
name: Pearson Max
- type: spearman_max
value: 0.850480817077266
name: Spearman Max
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts dev 64
type: sts-dev-64
metrics:
- type: pearson_cosine
value: 0.8176550213032908
name: Pearson Cosine
- type: spearman_cosine
value: 0.8307913870285043
name: Spearman Cosine
- type: pearson_manhattan
value: 0.8291830276998975
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.8320477651805375
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.8311109004860973
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.8333955109708812
name: Spearman Euclidean
- type: pearson_dot
value: 0.7153413665605783
name: Pearson Dot
- type: spearman_dot
value: 0.7181274999679498
name: Spearman Dot
- type: pearson_max
value: 0.8311109004860973
name: Pearson Max
- type: spearman_max
value: 0.8333955109708812
name: Spearman Max
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts test 768
type: sts-test-768
metrics:
- type: pearson_cosine
value: 0.8491592809545866
name: Pearson Cosine
- type: spearman_cosine
value: 0.8568871215102605
name: Spearman Cosine
- type: pearson_manhattan
value: 0.8572052385387118
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.856617432589014
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.8568623186549655
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.8567096295439565
name: Spearman Euclidean
- type: pearson_dot
value: 0.7968828934121807
name: Pearson Dot
- type: spearman_dot
value: 0.7879173370882538
name: Spearman Dot
- type: pearson_max
value: 0.8572052385387118
name: Pearson Max
- type: spearman_max
value: 0.8568871215102605
name: Spearman Max
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts test 512
type: sts-test-512
metrics:
- type: pearson_cosine
value: 0.8507070298067174
name: Pearson Cosine
- type: spearman_cosine
value: 0.8575370129160172
name: Spearman Cosine
- type: pearson_manhattan
value: 0.8564033014649287
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.8560352984315738
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.8561906595447021
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.8560701630452845
name: Spearman Euclidean
- type: pearson_dot
value: 0.7973312469719326
name: Pearson Dot
- type: spearman_dot
value: 0.7873345752731498
name: Spearman Dot
- type: pearson_max
value: 0.8564033014649287
name: Pearson Max
- type: spearman_max
value: 0.8575370129160172
name: Spearman Max
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts test 256
type: sts-test-256
metrics:
- type: pearson_cosine
value: 0.8467375811334358
name: Pearson Cosine
- type: spearman_cosine
value: 0.8523459221020806
name: Spearman Cosine
- type: pearson_manhattan
value: 0.8515524299355154
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.8516309696270962
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.8505975029491393
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.8504082169041302
name: Spearman Euclidean
- type: pearson_dot
value: 0.7756647219222156
name: Pearson Dot
- type: spearman_dot
value: 0.7687165011432322
name: Spearman Dot
- type: pearson_max
value: 0.8515524299355154
name: Pearson Max
- type: spearman_max
value: 0.8523459221020806
name: Spearman Max
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts test 128
type: sts-test-128
metrics:
- type: pearson_cosine
value: 0.8377317518267889
name: Pearson Cosine
- type: spearman_cosine
value: 0.84715184876888
name: Spearman Cosine
- type: pearson_manhattan
value: 0.846568244977152
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.8487991796570058
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.8456229087328332
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.847227591472
name: Spearman Euclidean
- type: pearson_dot
value: 0.7502527212449147
name: Pearson Dot
- type: spearman_dot
value: 0.7415962106597614
name: Spearman Dot
- type: pearson_max
value: 0.846568244977152
name: Pearson Max
- type: spearman_max
value: 0.8487991796570058
name: Spearman Max
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts test 64
type: sts-test-64
metrics:
- type: pearson_cosine
value: 0.8173604263806156
name: Pearson Cosine
- type: spearman_cosine
value: 0.8315612974155435
name: Spearman Cosine
- type: pearson_manhattan
value: 0.8319781289166863
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.8347311175148256
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.8334921243463637
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.8350960592133633
name: Spearman Euclidean
- type: pearson_dot
value: 0.6935445265890855
name: Pearson Dot
- type: spearman_dot
value: 0.6843746062699552
name: Spearman Dot
- type: pearson_max
value: 0.8334921243463637
name: Pearson Max
- type: spearman_max
value: 0.8350960592133633
name: Spearman Max
---
# SentenceTransformer based on l3cube-pune/indic-sentence-similarity-sbert
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [l3cube-pune/indic-sentence-similarity-sbert](https://huggingface.co/l3cube-pune/indic-sentence-similarity-sbert) 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:** [l3cube-pune/indic-sentence-similarity-sbert](https://huggingface.co/l3cube-pune/indic-sentence-similarity-sbert) <!-- at revision b07ef91a96390f3e35ce94ddb42340861519bf07 -->
- **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: 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("ammumadhu/indic-bert-nli-matryoshka")
# Run inference
sentences = [
'Then he ran.',
'He then started to run.',
'A man plays the flute.',
]
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 | 0.8609 |
| **spearman_cosine** | **0.8663** |
| pearson_manhattan | 0.8587 |
| spearman_manhattan | 0.8612 |
| pearson_euclidean | 0.8585 |
| spearman_euclidean | 0.8611 |
| pearson_dot | 0.8259 |
| spearman_dot | 0.826 |
| pearson_max | 0.8609 |
| spearman_max | 0.8663 |
#### 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 | 0.8594 |
| **spearman_cosine** | **0.8649** |
| pearson_manhattan | 0.8574 |
| spearman_manhattan | 0.8599 |
| pearson_euclidean | 0.8575 |
| spearman_euclidean | 0.8601 |
| pearson_dot | 0.8223 |
| spearman_dot | 0.8227 |
| pearson_max | 0.8594 |
| spearman_max | 0.8649 |
#### 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 | 0.8506 |
| **spearman_cosine** | **0.8576** |
| pearson_manhattan | 0.8528 |
| spearman_manhattan | 0.8553 |
| pearson_euclidean | 0.8527 |
| spearman_euclidean | 0.8551 |
| pearson_dot | 0.7944 |
| spearman_dot | 0.7964 |
| pearson_max | 0.8528 |
| spearman_max | 0.8576 |
#### 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 | 0.8411 |
| **spearman_cosine** | **0.8505** |
| pearson_manhattan | 0.8462 |
| spearman_manhattan | 0.849 |
| pearson_euclidean | 0.8458 |
| spearman_euclidean | 0.8487 |
| pearson_dot | 0.7756 |
| spearman_dot | 0.7756 |
| pearson_max | 0.8462 |
| spearman_max | 0.8505 |
#### 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 | 0.8177 |
| **spearman_cosine** | **0.8308** |
| pearson_manhattan | 0.8292 |
| spearman_manhattan | 0.832 |
| pearson_euclidean | 0.8311 |
| spearman_euclidean | 0.8334 |
| pearson_dot | 0.7153 |
| spearman_dot | 0.7181 |
| pearson_max | 0.8311 |
| spearman_max | 0.8334 |
#### Semantic Similarity
* Dataset: `sts-test-768`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.8492 |
| **spearman_cosine** | **0.8569** |
| pearson_manhattan | 0.8572 |
| spearman_manhattan | 0.8566 |
| pearson_euclidean | 0.8569 |
| spearman_euclidean | 0.8567 |
| pearson_dot | 0.7969 |
| spearman_dot | 0.7879 |
| pearson_max | 0.8572 |
| spearman_max | 0.8569 |
#### Semantic Similarity
* Dataset: `sts-test-512`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.8507 |
| **spearman_cosine** | **0.8575** |
| pearson_manhattan | 0.8564 |
| spearman_manhattan | 0.856 |
| pearson_euclidean | 0.8562 |
| spearman_euclidean | 0.8561 |
| pearson_dot | 0.7973 |
| spearman_dot | 0.7873 |
| pearson_max | 0.8564 |
| spearman_max | 0.8575 |
#### Semantic Similarity
* Dataset: `sts-test-256`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.8467 |
| **spearman_cosine** | **0.8523** |
| pearson_manhattan | 0.8516 |
| spearman_manhattan | 0.8516 |
| pearson_euclidean | 0.8506 |
| spearman_euclidean | 0.8504 |
| pearson_dot | 0.7757 |
| spearman_dot | 0.7687 |
| pearson_max | 0.8516 |
| spearman_max | 0.8523 |
#### Semantic Similarity
* Dataset: `sts-test-128`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.8377 |
| **spearman_cosine** | **0.8472** |
| pearson_manhattan | 0.8466 |
| spearman_manhattan | 0.8488 |
| pearson_euclidean | 0.8456 |
| spearman_euclidean | 0.8472 |
| pearson_dot | 0.7503 |
| spearman_dot | 0.7416 |
| pearson_max | 0.8466 |
| spearman_max | 0.8488 |
#### Semantic Similarity
* Dataset: `sts-test-64`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.8174 |
| **spearman_cosine** | **0.8316** |
| pearson_manhattan | 0.832 |
| spearman_manhattan | 0.8347 |
| pearson_euclidean | 0.8335 |
| spearman_euclidean | 0.8351 |
| pearson_dot | 0.6935 |
| spearman_dot | 0.6844 |
| pearson_max | 0.8335 |
| spearman_max | 0.8351 |
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## 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: 10,000 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: 4 tokens</li><li>mean: 18.8 tokens</li><li>max: 89 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 11.84 tokens</li><li>max: 36 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 12.39 tokens</li><li>max: 38 tokens</li></ul> |
* Samples:
| anchor | positive | negative |
|:----------------------------------------------------------------------------------------------------------|:--------------------------------------------------|:-------------------------------------------------------|
| <code>Side view of a female triathlete during the run.</code> | <code>A woman runs</code> | <code>A man sits</code> |
| <code>Confused person standing in the middle of the trolley tracks trying to figure out the signs.</code> | <code>A person is on the tracks.</code> | <code>A man sits in an airplane.</code> |
| <code>A woman in a black shirt, jean shorts and white tennis shoes is bowling.</code> | <code>A woman is bowling in casual clothes</code> | <code>A woman bowling wins an outfit of clothes</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: 18.54 tokens</li><li>max: 74 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 9.97 tokens</li><li>max: 30 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 10.59 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`: 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-256_spearman_cosine | sts-dev-512_spearman_cosine | sts-dev-64_spearman_cosine | sts-dev-768_spearman_cosine | sts-test-128_spearman_cosine | sts-test-256_spearman_cosine | sts-test-512_spearman_cosine | sts-test-64_spearman_cosine | sts-test-768_spearman_cosine |
|:------:|:----:|:-------------:|:------:|:---------------------------:|:---------------------------:|:---------------------------:|:--------------------------:|:---------------------------:|:----------------------------:|:----------------------------:|:----------------------------:|:---------------------------:|:----------------------------:|
| 0.3797 | 30 | 7.9432 | 4.2806 | 0.8509 | 0.8570 | 0.8633 | 0.8311 | 0.8644 | - | - | - | - | - |
| 0.7595 | 60 | 6.1701 | 3.9498 | 0.8505 | 0.8576 | 0.8649 | 0.8308 | 0.8663 | - | - | - | - | - |
| 1.0 | 79 | - | - | - | - | - | - | - | 0.8472 | 0.8523 | 0.8575 | 0.8316 | 0.8569 |
### 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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