metadata

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 model finetuned from ielabgroup/bert-base-uncased-fineweb100bt-matryoshka-mae on the 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
Maximum Sequence Length: 512 tokens
Output Dimensionality: 768 tokens
Similarity Function: Cosine Similarity
Training Dataset:
- all-nli
Language: en

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}) 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:

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("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]

Evaluation

Metrics

Semantic Similarity

Dataset: sts-test
Evaluated with 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

Training Details

Training Dataset

all-nli

Dataset: all-nli at d482672
Size: 557,850 training samples
Columns: anchor, positive, and negative

Approximate statistics based on the first 1000 samples:

	anchor	positive	negative
type	string	string	string
details	min: 7 tokens mean: 10.46 tokens max: 46 tokens	min: 6 tokens mean: 12.81 tokens max: 40 tokens	min: 5 tokens mean: 13.4 tokens max: 50 tokens

Samples:

anchor	positive	negative
`A person on a horse jumps over a broken down airplane.`	`A person is outdoors, on a horse.`	`A person is at a diner, ordering an omelette.`
`Children smiling and waving at camera`	`There are children present`	`The kids are frowning`
`A boy is jumping on skateboard in the middle of a red bridge.`	`The boy does a skateboarding trick.`	`The boy skates down the sidewalk.`

Loss: starbucks_loss.StarbucksLoss with these parameters:

{
    "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

Click to expand

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

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

@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",
}