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---
base_model:
- ielabgroup/bert-base-uncased-fineweb100bt-smae
datasets:
- sentence-transformers/all-nli
language:
- en
library_name: sentence-transformers
metrics:
- pearson_cosine
- spearman_cosine
- pearson_manhattan
- spearman_manhattan
- pearson_euclidean
- spearman_euclidean
- pearson_dot
- spearman_dot
- pearson_max
- spearman_max
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:557850
- loss:StarbucksLoss
widget:
- source_sentence: A dog is in the water.
sentences:
- The woman is wearing green.
- The dog is rolling around in the grass.
- A brown dog swims through water outdoors with a tennis ball in its mouth.
- source_sentence: A dog is swimming.
sentences:
- a black dog swimming in the water with a tennis ball in his mouth
- A dog with yellow fur swims, neck deep, in water.
- A brown dog running through a large orange tube.
- source_sentence: A dog is swimming.
sentences:
- A dog with golden hair swims through water.
- A golden haired dog is lying in a boat that is traveling on a lake.
- A dog with golden hair swims through water.
- source_sentence: A dog is swimming.
sentences:
- A tan dog splashes as he swims through the water.
- A man and young boy asleep in a chair.
- A dog in a harness chasing a red ball.
- source_sentence: A dog is in the water.
sentences:
- A big brown dog jumps into a swimming pool on the backyard.
- Wet brown dog swims towards camera.
- The dog is rolling around in the grass.
model-index:
- name: >-
SentenceTransformer based on
ielabgroup/bert-base-uncased-fineweb100bt-matryoshka-mae
results:
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts test
type: sts-test
metrics:
- type: pearson_cosine
value: 0.8170317205826663
name: Pearson Cosine
- type: spearman_cosine
value: 0.827406310000667
name: Spearman Cosine
- type: pearson_manhattan
value: 0.8085162876731988
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.8050045835065848
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.8122787407180172
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.809299222491485
name: Spearman Euclidean
- type: pearson_dot
value: 0.7657571947414553
name: Pearson Dot
- type: spearman_dot
value: 0.7564706925314776
name: Spearman Dot
- type: pearson_max
value: 0.8170317205826663
name: Pearson Max
- type: spearman_max
value: 0.827406310000667
name: Spearman Max
---
# SentenceTransformer based on ielabgroup/bert-base-uncased-fineweb100bt-matryoshka-mae
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [ielabgroup/bert-base-uncased-fineweb100bt-matryoshka-mae](https://huggingface.co/ielabgroup/bert-base-uncased-fineweb100bt-matryoshka-mae) on the [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:** [ielabgroup/bert-base-uncased-fineweb100bt-matryoshka-mae](https://huggingface.co/ielabgroup/bert-base-uncased-fineweb100bt-matryoshka-mae) <!-- at revision 5ad87b09309fdc0a114357f37b45c4de7e4dcec6 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 tokens
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
- [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("ielabgroup/Starbucks_STS")
# Run inference
sentences = [
'A dog is in the water.',
'Wet brown dog swims towards camera.',
'The dog is rolling around in the grass.',
]
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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### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
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## Evaluation
### Metrics
#### Semantic Similarity
* Dataset: `sts-test`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.817 |
| **spearman_cosine** | **0.8274** |
| pearson_manhattan | 0.8085 |
| spearman_manhattan | 0.805 |
| pearson_euclidean | 0.8123 |
| spearman_euclidean | 0.8093 |
| pearson_dot | 0.7658 |
| spearman_dot | 0.7565 |
| pearson_max | 0.817 |
| spearman_max | 0.8274 |
<!--
## 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.*
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### Recommendations
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## Training Details
### Training Dataset
#### all-nli
* Dataset: [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: 10.46 tokens</li><li>max: 46 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 12.81 tokens</li><li>max: 40 tokens</li></ul> | <ul><li>min: 5 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>starbucks_loss.StarbucksLoss</code> with these parameters:
```json
{
"loss": "MatryoshkaLoss",
"n_selections_per_step": -1,
"last_layer_weight": 1.0,
"prior_layers_weight": 1.0,
"kl_div_weight": 1.0,
"kl_temperature": 0.3,
"matryoshka_layers": [
1,
3,
5,
7,
9,
11
],
"matryoshka_dims": [
32,
64,
128,
256,
512,
768
]
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `per_device_train_batch_size`: 128
- `per_device_eval_batch_size`: 128
- `num_train_epochs`: 1
- `warmup_ratio`: 0.1
- `fp16`: True
- `gradient_checkpointing`: True
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: no
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 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
- `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`: 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`: True
- `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
- `eval_on_start`: False
- `eval_use_gather_object`: False
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
| Epoch | Step | Training Loss | sts-test_spearman_cosine |
|:------:|:----:|:-------------:|:------------------------:|
| 0.0229 | 100 | 16.7727 | - |
| 0.0459 | 200 | 9.653 | - |
| 0.0688 | 300 | 8.3187 | - |
| 0.0918 | 400 | 7.748 | - |
| 0.1147 | 500 | 7.2587 | - |
| 0.1376 | 600 | 6.734 | - |
| 0.1606 | 700 | 6.4463 | - |
| 0.1835 | 800 | 6.299 | - |
| 0.2065 | 900 | 5.9946 | - |
| 0.2294 | 1000 | 5.9348 | - |
| 0.2524 | 1100 | 5.7723 | - |
| 0.2753 | 1200 | 5.5822 | - |
| 0.2982 | 1300 | 5.4233 | - |
| 0.3212 | 1400 | 5.3427 | - |
| 0.3441 | 1500 | 5.3132 | - |
| 0.3671 | 1600 | 5.3149 | - |
| 0.3900 | 1700 | 5.3007 | - |
| 0.4129 | 1800 | 4.9539 | - |
| 0.4359 | 1900 | 4.9308 | - |
| 0.4588 | 2000 | 4.8171 | - |
| 0.4818 | 2100 | 5.0181 | - |
| 0.5047 | 2200 | 4.9631 | - |
| 0.5276 | 2300 | 4.8125 | - |
| 0.5506 | 2400 | 4.7133 | - |
| 0.5735 | 2500 | 4.5809 | - |
| 0.5965 | 2600 | 4.6093 | - |
| 0.6194 | 2700 | 4.6723 | - |
| 0.6423 | 2800 | 4.5526 | - |
| 0.6653 | 2900 | 4.4967 | - |
| 0.6882 | 3000 | 4.4178 | - |
| 0.7112 | 3100 | 4.4333 | - |
| 0.7341 | 3200 | 4.3289 | - |
| 0.7571 | 3300 | 4.5199 | - |
| 0.7800 | 3400 | 4.3389 | - |
| 0.8029 | 3500 | 4.3394 | - |
| 0.8259 | 3600 | 4.2423 | - |
| 0.8488 | 3700 | 4.3219 | - |
| 0.8718 | 3800 | 4.3297 | - |
| 0.8947 | 3900 | 4.3132 | - |
| 0.9176 | 4000 | 4.2616 | - |
| 0.9406 | 4100 | 4.2233 | - |
| 0.9635 | 4200 | 4.1912 | - |
| 0.9865 | 4300 | 4.1838 | - |
| 1.0 | 4359 | - | 0.8274 |
### Framework Versions
- Python: 3.10.13
- Sentence Transformers: 3.1.1
- Transformers: 4.44.2
- PyTorch: 2.4.1+cu121
- Accelerate: 0.33.0
- Datasets: 2.21.0
- 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",
}
```
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