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---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:200000
- loss:MSELoss
base_model: nreimers/TinyBERT_L-4_H-312_v2
widget:
- source_sentence: At an outdoor event in an Asian-themed area, a crowd congregates
as one person in a yellow Chinese dragon costume confronts the camera.
sentences:
- Boy dressed in blue holds a toy.
- the animal is running
- Two young asian men are squatting.
- source_sentence: A man with a shopping cart is studying the shelves in a supermarket
aisle.
sentences:
- The children are watching TV at home.
- Three young boys one is holding a camera and another is holding a green toy all
are wearing t-shirt and smiling.
- A large group of people are gathered outside of a brick building lit with spotlights.
- source_sentence: The door is open.
sentences:
- There are three men in this picture, two are on motorbikes, one of the men has
a large piece of furniture on the back of his bike, the other is about to be handed
a piece of paper by a man in a white shirt.
- People are playing music.
- A girl is using an apple laptop with her headphones in her ears.
- source_sentence: A small group of children are standing in a classroom and one of
them has a foot in a trashcan, which also has a rope leading out of it.
sentences:
- Children are swimming at the beach.
- Women are celebrating at a bar.
- Some men with jerseys are in a bar, watching a soccer match.
- source_sentence: A black dog is drinking next to a brown and white dog that is looking
at an orange ball in the lake, whilst a horse and rider passes behind.
sentences:
- There are two people running around a track in lane three and the one wearing
a blue shirt with a green thing over the eyes is just barely ahead of the guy
wearing an orange shirt and sunglasses.
- A girl is sitting
- the guy is dead
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- pearson_cosine
- spearman_cosine
- negative_mse
co2_eq_emissions:
emissions: 3.4513310599379015
energy_consumed: 0.008879118347571923
source: codecarbon
training_type: fine-tuning
on_cloud: false
cpu_model: 13th Gen Intel(R) Core(TM) i7-13700K
ram_total_size: 31.777088165283203
hours_used: 0.053
hardware_used: 1 x NVIDIA GeForce RTX 3090
model-index:
- name: SentenceTransformer based on nreimers/TinyBERT_L-4_H-312_v2
results:
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts dev
type: sts-dev
metrics:
- type: pearson_cosine
value: 0.8020427163636963
name: Pearson Cosine
- type: spearman_cosine
value: 0.8162119531251948
name: Spearman Cosine
- task:
type: knowledge-distillation
name: Knowledge Distillation
dataset:
name: Unknown
type: unknown
metrics:
- type: negative_mse
value: -50.39951801300049
name: Negative Mse
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts test
type: sts-test
metrics:
- type: pearson_cosine
value: 0.7493791518293895
name: Pearson Cosine
- type: spearman_cosine
value: 0.752488836028113
name: Spearman Cosine
---
# SentenceTransformer based on nreimers/TinyBERT_L-4_H-312_v2
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [nreimers/TinyBERT_L-4_H-312_v2](https://huggingface.co/nreimers/TinyBERT_L-4_H-312_v2). It maps sentences & paragraphs to a 312-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:** [nreimers/TinyBERT_L-4_H-312_v2](https://huggingface.co/nreimers/TinyBERT_L-4_H-312_v2) <!-- at revision d782507ee95c6565fe5924fcd6090999055e8db6 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 312 dimensions
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 312, '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("tomaarsen/TinyBERT_L-4_H-312_v2-distilled-from-stsb-roberta-base-v2-new")
# Run inference
sentences = [
'A black dog is drinking next to a brown and white dog that is looking at an orange ball in the lake, whilst a horse and rider passes behind.',
'There are two people running around a track in lane three and the one wearing a blue shirt with a green thing over the eyes is just barely ahead of the guy wearing an orange shirt and sunglasses.',
'the guy is dead',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 312]
# 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
* Datasets: `sts-dev` and `sts-test`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | sts-dev | sts-test |
|:--------------------|:-----------|:-----------|
| pearson_cosine | 0.802 | 0.7494 |
| **spearman_cosine** | **0.8162** | **0.7525** |
#### Knowledge Distillation
* Evaluated with [<code>MSEEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.MSEEvaluator)
| Metric | Value |
|:-----------------|:-------------|
| **negative_mse** | **-50.3995** |
<!--
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*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
#### Unnamed Dataset
* Size: 200,000 training samples
* Columns: <code>sentence</code> and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | sentence | label |
|:--------|:----------------------------------------------------------------------------------|:-------------------------------------|
| type | string | list |
| details | <ul><li>min: 4 tokens</li><li>mean: 12.24 tokens</li><li>max: 52 tokens</li></ul> | <ul><li>size: 312 elements</li></ul> |
* Samples:
| sentence | label |
|:---------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------|
| <code>A person on a horse jumps over a broken down airplane.</code> | <code>[-0.05779948830604553, 0.7306336760520935, -2.7011518478393555, 1.7303822040557861, 1.379652500152588, ...]</code> |
| <code>Children smiling and waving at camera</code> | <code>[-2.939552068710327, 2.887307643890381, 7.378897666931152, 5.352669715881348, -2.55843448638916, ...]</code> |
| <code>A boy is jumping on skateboard in the middle of a red bridge.</code> | <code>[2.7139971256256104, 3.2107176780700684, 1.0811409950256348, 6.389298439025879, -0.5123305320739746, ...]</code> |
* Loss: [<code>MSELoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#mseloss)
### Evaluation Dataset
#### Unnamed Dataset
* Size: 10,000 evaluation samples
* Columns: <code>sentence</code> and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | sentence | label |
|:--------|:----------------------------------------------------------------------------------|:-------------------------------------|
| type | string | list |
| details | <ul><li>min: 5 tokens</li><li>mean: 13.23 tokens</li><li>max: 57 tokens</li></ul> | <ul><li>size: 312 elements</li></ul> |
* Samples:
| sentence | label |
|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------|
| <code>Two women are embracing while holding to go packages.</code> | <code>[-5.986438751220703, -2.4999303817749023, 2.2099857330322266, -2.048459529876709, 1.1695001125335693, ...]</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>[-1.8326359987258911, 0.5514901876449585, 2.561642646789551, 3.8372995853424072, -3.0104174613952637, ...]</code> |
| <code>A man selling donuts to a customer during a world exhibition event held in the city of Angeles</code> | <code>[3.0850987434387207, 3.353701591491699, -0.2763029932975769, -2.3397164344787598, 3.109376907348633, ...]</code> |
* Loss: [<code>MSELoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#mseloss)
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 64
- `per_device_eval_batch_size`: 64
- `learning_rate`: 0.0001
- `num_train_epochs`: 1
- `warmup_ratio`: 0.1
- `fp16`: True
- `load_best_model_at_end`: True
#### 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`: 64
- `per_device_eval_batch_size`: 64
- `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`: 0.0001
- `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`: True
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: None
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
| Epoch | Step | Training Loss | Validation Loss | sts-dev_spearman_cosine | negative_mse | sts-test_spearman_cosine |
|:--------:|:--------:|:-------------:|:---------------:|:-----------------------:|:------------:|:------------------------:|
| 0.032 | 100 | 0.885 | - | - | - | - |
| 0.064 | 200 | 0.7985 | - | - | - | - |
| 0.096 | 300 | 0.6881 | - | - | - | - |
| 0.128 | 400 | 0.6088 | - | - | - | - |
| 0.16 | 500 | 0.5608 | 0.6318 | 0.7526 | -63.1827 | - |
| 0.192 | 600 | 0.5278 | - | - | - | - |
| 0.224 | 700 | 0.5031 | - | - | - | - |
| 0.256 | 800 | 0.4854 | - | - | - | - |
| 0.288 | 900 | 0.4659 | - | - | - | - |
| 0.32 | 1000 | 0.4514 | 0.5661 | 0.7928 | -56.6129 | - |
| 0.352 | 1100 | 0.4373 | - | - | - | - |
| 0.384 | 1200 | 0.427 | - | - | - | - |
| 0.416 | 1300 | 0.4181 | - | - | - | - |
| 0.448 | 1400 | 0.41 | - | - | - | - |
| 0.48 | 1500 | 0.4053 | 0.5370 | 0.8043 | -53.6980 | - |
| 0.512 | 1600 | 0.3934 | - | - | - | - |
| 0.544 | 1700 | 0.3905 | - | - | - | - |
| 0.576 | 1800 | 0.3848 | - | - | - | - |
| 0.608 | 1900 | 0.3787 | - | - | - | - |
| 0.64 | 2000 | 0.3734 | 0.5192 | 0.8099 | -51.9208 | - |
| 0.672 | 2100 | 0.3715 | - | - | - | - |
| 0.704 | 2200 | 0.3694 | - | - | - | - |
| 0.736 | 2300 | 0.3665 | - | - | - | - |
| 0.768 | 2400 | 0.3615 | - | - | - | - |
| 0.8 | 2500 | 0.3576 | 0.5101 | 0.8147 | -51.0102 | - |
| 0.832 | 2600 | 0.3547 | - | - | - | - |
| 0.864 | 2700 | 0.3542 | - | - | - | - |
| 0.896 | 2800 | 0.3521 | - | - | - | - |
| 0.928 | 2900 | 0.352 | - | - | - | - |
| **0.96** | **3000** | **0.3525** | **0.504** | **0.8162** | **-50.3995** | **-** |
| 0.992 | 3100 | 0.3491 | - | - | - | - |
| -1 | -1 | - | - | - | - | 0.7525 |
* The bold row denotes the saved checkpoint.
### Environmental Impact
Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
- **Energy Consumed**: 0.009 kWh
- **Carbon Emitted**: 0.003 kg of CO2
- **Hours Used**: 0.053 hours
### Training Hardware
- **On Cloud**: No
- **GPU Model**: 1 x NVIDIA GeForce RTX 3090
- **CPU Model**: 13th Gen Intel(R) Core(TM) i7-13700K
- **RAM Size**: 31.78 GB
### Framework Versions
- Python: 3.11.6
- Sentence Transformers: 3.5.0.dev0
- Transformers: 4.49.0
- PyTorch: 2.6.0+cu124
- Accelerate: 1.5.1
- Datasets: 3.3.2
- Tokenizers: 0.21.0
## Citation
### BibTeX
#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084",
}
```
#### MSELoss
```bibtex
@inproceedings{reimers-2020-multilingual-sentence-bert,
title = "Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2020",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/2004.09813",
}
```
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