Add new SentenceTransformer model

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metadata

tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - dense
  - 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:
      - 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.
      - >-
        Three girls are standing together in a room, one is listening, one is
        writing on a wall and the third is talking to them.
  - 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
co2_eq_emissions:
  emissions: 2.8523555208748004
  energy_consumed: 0.010658150379545777
  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: 0.06
  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: 0.808118809417341
            name: Pearson Cosine
          - type: spearman_cosine
            value: 0.8213188524284026
            name: Spearman Cosine
      - task:
          type: knowledge-distillation
          name: Knowledge Distillation
        dataset:
          name: Unknown
          type: unknown
        metrics:
          - type: negative_mse
            value: -78.18629741668701
            name: Negative Mse
      - task:
          type: semantic-similarity
          name: Semantic Similarity
        dataset:
          name: sts test
          type: sts-test
        metrics:
          - type: pearson_cosine
            value: 0.7496317179823885
            name: Pearson Cosine
          - type: spearman_cosine
            value: 0.7520734191438844
            name: Spearman Cosine

SentenceTransformer based on nreimers/TinyBERT_L-4_H-312_v2

This is a sentence-transformers model finetuned from 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
Maximum Sequence Length: 512 tokens
Output Dimensionality: 312 dimensions
Similarity Function: Cosine Similarity

Model Sources

Documentation: Sentence Transformers Documentation
Repository: Sentence Transformers on GitHub
Hugging Face: Sentence Transformers on Hugging Face

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False, 'architecture': '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})
  (2): Normalize()
)

Usage

Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

pip install -U sentence-transformers

Then you can load this model and run inference.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("tomaarsen/TinyBERT_L-4_H-312_v2-distilled-from-stsb-roberta-base-v2-L2-normalized")
# 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, 312]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[ 1.0000, -0.0448,  0.0490],
#         [-0.0448,  1.0000,  0.3433],
#         [ 0.0490,  0.3433,  1.0000]])

Evaluation

Metrics

Semantic Similarity

Datasets: sts-dev and sts-test
Evaluated with EmbeddingSimilarityEvaluator

Metric	sts-dev	sts-test
pearson_cosine	0.8081	0.7496
spearman_cosine	0.8213	0.7521

Knowledge Distillation

Evaluated with MSEEvaluator

Metric	Value
negative_mse	-78.1863

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

size: 312 elements

	sentence	label
type	string	list
details	min: 4 tokens mean: 12.24 tokens max: 52 tokens	size: 312 elements

Samples:

sentence	label
`A person on a horse jumps over a broken down airplane.`	`[0.005435045808553696, 0.042343951761722565, -0.1521506905555725, 0.10078108310699463, 0.06585284322500229, ...]`
`Children smiling and waving at camera`	`[-0.14921779930591583, 0.17264199256896973, 0.3912944793701172, 0.2817707657814026, -0.1517026573419571, ...]`
`A boy is jumping on skateboard in the middle of a red bridge.`	`[0.1835351139307022, 0.17705069482326508, 0.07568985968828201, 0.37269654870033264, -0.04005592316389084, ...]`

Loss: 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

size: 312 elements

	sentence	label
type	string	list
details	min: 5 tokens mean: 13.23 tokens max: 57 tokens	size: 312 elements

Samples:

sentence	label
`Two women are embracing while holding to go packages.`	`[-0.3905734717845917, -0.12415792047977448, 0.13489434123039246, -0.13027705252170563, 0.09115917980670929, ...]`
`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.`	`[-0.11542800068855286, 0.039172928780317307, 0.16285762190818787, 0.2441333532333374, -0.1625598669052124, ...]`
`A man selling donuts to a customer during a world exhibition event held in the city of Angeles`	`[0.20657853782176971, 0.18892505764961243, -0.011273683980107307, -0.15149112045764923, 0.18431058526039124, ...]`

Loss: 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
hub_revision: None
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
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
liger_kernel_config: None
eval_use_gather_object: False
average_tokens_across_devices: False
prompts: None
batch_sampler: batch_sampler
multi_dataset_batch_sampler: proportional
router_mapping: {}
learning_rate_mapping: {}

Training Logs

Epoch	Step	Training Loss	Validation Loss	sts-dev_spearman_cosine	negative_mse	sts-test_spearman_cosine
0.032	100	1.3189	-	-	-	-
0.064	200	1.1638	-	-	-	-
0.096	300	1.0132	-	-	-	-
0.128	400	0.924	-	-	-	-
0.16	500	0.8666	0.9844	0.7668	-79.6441	-
0.192	600	0.8265	-	-	-	-
0.224	700	0.7954	-	-	-	-
0.256	800	0.7749	-	-	-	-
0.288	900	0.7542	-	-	-	-
0.32	1000	0.7318	0.8942	0.7966	-78.8491	-
0.352	1100	0.7207	-	-	-	-
0.384	1200	0.7085	-	-	-	-
0.416	1300	0.6996	-	-	-	-
0.448	1400	0.6889	-	-	-	-
0.48	1500	0.6823	0.8533	0.8125	-78.5136	-
0.512	1600	0.6704	-	-	-	-
0.544	1700	0.6662	-	-	-	-
0.576	1800	0.6587	-	-	-	-
0.608	1900	0.6515	-	-	-	-
0.64	2000	0.6479	0.8323	0.8160	-78.3540	-
0.672	2100	0.6463	-	-	-	-
0.704	2200	0.6423	-	-	-	-
0.736	2300	0.6379	-	-	-	-
0.768	2400	0.6343	-	-	-	-
0.8	2500	0.6309	0.8185	0.8196	-78.2491	-
0.832	2600	0.6275	-	-	-	-
0.864	2700	0.628	-	-	-	-
0.896	2800	0.6232	-	-	-	-
0.928	2900	0.623	-	-	-	-
0.96	3000	0.6229	0.8107	0.8213	-78.1863	-
0.992	3100	0.6208	-	-	-	-
-1	-1	-	-	-	-	0.7521

The bold row denotes the saved checkpoint.

Environmental Impact

Carbon emissions were measured using CodeCarbon.

Energy Consumed: 0.011 kWh
Carbon Emitted: 0.003 kg of CO2
Hours Used: 0.06 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: 5.2.0.dev0
Transformers: 4.53.3
PyTorch: 2.8.0+cu128
Accelerate: 1.6.0
Datasets: 4.2.0
Tokenizers: 0.21.4

Citation

BibTeX

Sentence Transformers

@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

@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",
}