---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:1195425
- loss:MSELoss
base_model: mixedbread-ai/mxbai-embed-large-v1
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.
- A man is smiling at his wife.
- Two young asian men are squatting.
- source_sentence: A man with a shopping cart is studying the shelves in a supermarket aisle.
sentences:
- the animal is running
- 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.
- source_sentence: The door is open.
sentences:
- A girl is using an apple laptop with her headphones in her ears.
- >-
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.
- >-
A large group of people are gathered outside of a brick building lit with
spotlights.
- 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:
- People are playing music.
- Children are swimming at the beach.
- Women are celebrating at a bar.
- 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:
- Some men with jerseys are in a bar, watching a soccer match.
- the guy is dead
- >-
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.
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- pearson_cosine
- spearman_cosine
- negative_mse
model-index:
- name: SentenceTransformer based on mixedbread-ai/mxbai-embed-large-v1
results:
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts dev
type: sts-dev
metrics:
- type: pearson_cosine
value: 0.8654028138219636
name: Pearson Cosine
- type: spearman_cosine
value: 0.8873087539713633
name: Spearman Cosine
- task:
type: knowledge-distillation
name: Knowledge Distillation
dataset:
name: Unknown
type: unknown
metrics:
- type: negative_mse
value: -3.3795181661844254
name: Negative Mse
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts test
type: sts-test
metrics:
- type: pearson_cosine
value: 0.834023412201456
name: Pearson Cosine
- type: spearman_cosine
value: 0.8723901159121923
name: Spearman Cosine
license: apache-2.0
language:
- en
---
# SentenceTransformer based on Model Distillation
In this experiment with knowledge distillation for embedding models, i retained 8 layers from the teacher model. This is an attempt to create a lighter, faster version.
- the top left graph shows how well your model's predictions match reality. Spearman correlation = 0.887
- the top right compares the correlation performance of this model vs the reference(mxbai-embed-large-v1) model - both bars around 0.8-0.9
- bottom left shows, this model processes about 45 samples/s and mxbai-embed-large-v1 processes about 30 samples/s.
- the bottom right shows a small accuracy drop for this model.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [mixedbread-ai/mxbai-embed-large-v1](https://huggingface.co/mixedbread-ai/mxbai-embed-large-v1)
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 1024 dimensions
- **Similarity Function:** Cosine Similarity
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, '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 = [
'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.',
'Some men with jerseys are in a bar, watching a soccer match.',
'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.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# 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 | 0.8654 | 0.834 |
| **spearman_cosine** | **0.8873** | **0.8724** |
#### Knowledge Distillation
* Evaluated with [MSEEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.MSEEvaluator)
| Metric | Value |
|:-----------------|:------------|
| **negative_mse** | **-3.3795** |
## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 1,195,425 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. | [-0.012967385351657867, 0.3716000020503998, 0.252520889043808, 0.7052643299102783, -0.15118499100208282, ...] |
| Children smiling and waving at camera | [0.15414997935295105, 0.6666896939277649, -0.3150098919868469, 1.0102407932281494, 0.4113735556602478, ...] |
| A boy is jumping on skateboard in the middle of a red bridge. | [-0.2989530563354492, 0.8571284413337708, -0.48532426357269287, 0.8935043215751648, 0.28524795174598694, ...] |
* 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. | [-0.35094621777534485, 0.4337681233882904, 0.22905530035495758, 0.9438946843147278, -1.0199058055877686, ...] |
| 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. | [-0.37593328952789307, 0.6690596342086792, -0.14921458065509796, 0.7559019923210144, -0.4093412756919861, ...] |
| A man selling donuts to a customer during a world exhibition event held in the city of Angeles | [0.21969863772392273, 0.5065202713012695, -0.25664886832237244, 0.2569092810153961, -0.05940837413072586, ...] |
* 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`: False
- `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