---
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)
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 312 dimensions
- **Similarity Function:** Cosine Similarity
### 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]
```
## Evaluation
### Metrics
#### Semantic Similarity
* Datasets: `sts-dev` and `sts-test`
* Evaluated with [EmbeddingSimilarityEvaluator](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 [MSEEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.MSEEvaluator)
| Metric | Value |
|:-----------------|:-------------|
| **negative_mse** | **-50.3995** |
## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 200,000 training samples
* Columns: sentence and label
* Approximate statistics based on the first 1000 samples:
| | sentence | label |
|:--------|:----------------------------------------------------------------------------------|:-------------------------------------|
| type | string | list |
| details | - min: 4 tokens
- mean: 12.24 tokens
- max: 52 tokens
| |
* Samples:
| sentence | label |
|:---------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------|
| A person on a horse jumps over a broken down airplane. | [-0.05779948830604553, 0.7306336760520935, -2.7011518478393555, 1.7303822040557861, 1.379652500152588, ...] |
| Children smiling and waving at camera | [-2.939552068710327, 2.887307643890381, 7.378897666931152, 5.352669715881348, -2.55843448638916, ...] |
| A boy is jumping on skateboard in the middle of a red bridge. | [2.7139971256256104, 3.2107176780700684, 1.0811409950256348, 6.389298439025879, -0.5123305320739746, ...] |
* Loss: [MSELoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#mseloss)
### Evaluation Dataset
#### Unnamed Dataset
* Size: 10,000 evaluation samples
* Columns: sentence and label
* Approximate statistics based on the first 1000 samples:
| | sentence | label |
|:--------|:----------------------------------------------------------------------------------|:-------------------------------------|
| type | string | list |
| details | - min: 5 tokens
- mean: 13.23 tokens
- max: 57 tokens
| |
* Samples:
| sentence | label |
|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------|
| Two women are embracing while holding to go packages. | [-5.986438751220703, -2.4999303817749023, 2.2099857330322266, -2.048459529876709, 1.1695001125335693, ...] |
| 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. | [-1.8326359987258911, 0.5514901876449585, 2.561642646789551, 3.8372995853424072, -3.0104174613952637, ...] |
| A man selling donuts to a customer during a world exhibition event held in the city of Angeles | [3.0850987434387207, 3.353701591491699, -0.2763029932975769, -2.3397164344787598, 3.109376907348633, ...] |
* Loss: [MSELoss](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
Click to expand
- `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