metadata

language:
  - en
library_name: sentence-transformers
tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - dataset_size:100K<n<1M
  - loss:MSELoss
base_model: w601sxs/b1ade-embed
metrics:
  - pearson_cosine
  - spearman_cosine
  - pearson_manhattan
  - spearman_manhattan
  - pearson_euclidean
  - spearman_euclidean
  - pearson_dot
  - spearman_dot
  - pearson_max
  - spearman_max
  - negative_mse
widget:
  - source_sentence: A man is jumping.
    sentences:
      - The man is jumping off something.
      - Two people are posing for a photograph.
      - two women sing opera
  - source_sentence: The wave is huge.
    sentences:
      - A person is surfing on a large wave.
      - People are competing in figure skating.
      - Cats are sleeping inside the room.
  - source_sentence: The man is short.
    sentences:
      - There is a man vaucuming
      - The man did a self portrait of himself.
      - The boys are asleep in their beds.
  - source_sentence: A boy is bowling.
    sentences:
      - A boy is rolling a ball in a hotel hallway.
      - PHS enrolls approximately 750 students.
      - The older men are talking about their wives.
  - source_sentence: A man is walking
    sentences:
      - The man is going for a walk.
      - The station opened on 1 December 1896.
      - The woman is alone and asleep in the car on the moon.
pipeline_tag: sentence-similarity
model-index:
  - name: SentenceTransformer based on w601sxs/b1ade-embed
    results:
      - task:
          type: semantic-similarity
          name: Semantic Similarity
        dataset:
          name: sts dev
          type: sts-dev
        metrics:
          - type: pearson_cosine
            value: 0.6737565660591995
            name: Pearson Cosine
          - type: spearman_cosine
            value: 0.7346594963661589
            name: Spearman Cosine
          - type: pearson_manhattan
            value: 0.700631080294873
            name: Pearson Manhattan
          - type: spearman_manhattan
            value: 0.7089388326911368
            name: Spearman Manhattan
          - type: pearson_euclidean
            value: 0.7016605503100202
            name: Pearson Euclidean
          - type: spearman_euclidean
            value: 0.7101559719602629
            name: Spearman Euclidean
          - type: pearson_dot
            value: 0.7336031520397918
            name: Pearson Dot
          - type: spearman_dot
            value: 0.7509506568007358
            name: Spearman Dot
          - type: pearson_max
            value: 0.7336031520397918
            name: Pearson Max
          - type: spearman_max
            value: 0.7509506568007358
            name: Spearman Max
      - task:
          type: knowledge-distillation
          name: Knowledge Distillation
        dataset:
          name: Unknown
          type: unknown
        metrics:
          - type: negative_mse
            value: -21.545076370239258
            name: Negative Mse
      - task:
          type: semantic-similarity
          name: Semantic Similarity
        dataset:
          name: sts test
          type: sts-test
        metrics:
          - type: pearson_cosine
            value: 0.677225151823628
            name: Pearson Cosine
          - type: spearman_cosine
            value: 0.7310810412009605
            name: Spearman Cosine
          - type: pearson_manhattan
            value: 0.7076654744568199
            name: Pearson Manhattan
          - type: spearman_manhattan
            value: 0.7120808159972457
            name: Spearman Manhattan
          - type: pearson_euclidean
            value: 0.7070890827522099
            name: Pearson Euclidean
          - type: spearman_euclidean
            value: 0.7115055158750536
            name: Spearman Euclidean
          - type: pearson_dot
            value: 0.7026111016442886
            name: Pearson Dot
          - type: spearman_dot
            value: 0.6949199269988278
            name: Spearman Dot
          - type: pearson_max
            value: 0.7076654744568199
            name: Pearson Max
          - type: spearman_max
            value: 0.7310810412009605
            name: Spearman Max

SentenceTransformer based on w601sxs/b1ade-embed

This is a sentence-transformers model finetuned from w601sxs/b1ade-embed on the sentence-transformers/wikipedia-en-sentences dataset. It maps sentences & paragraphs to a 1024-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: w601sxs/b1ade-embed
Maximum Sequence Length: 512 tokens
Output Dimensionality: 1024 tokens
Similarity Function: Cosine Similarity
Training Dataset:
- sentence-transformers/wikipedia-en-sentences
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': 1024, '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("w601sxs/b1ade-embed-distilled-from-gte-large-en-v1.5")
# Run inference
sentences = [
    'A man is walking',
    'The man is going for a walk.',
    'The station opened on 1 December 1896.',
]
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

Dataset: sts-dev
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.6738
spearman_cosine	0.7347
pearson_manhattan	0.7006
spearman_manhattan	0.7089
pearson_euclidean	0.7017
spearman_euclidean	0.7102
pearson_dot	0.7336
spearman_dot	0.751
pearson_max	0.7336
spearman_max	0.751

Knowledge Distillation

Evaluated with MSEEvaluator

Metric	Value
negative_mse	-21.5451

Semantic Similarity

Dataset: sts-test
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.6772
spearman_cosine	0.7311
pearson_manhattan	0.7077
spearman_manhattan	0.7121
pearson_euclidean	0.7071
spearman_euclidean	0.7115
pearson_dot	0.7026
spearman_dot	0.6949
pearson_max	0.7077
spearman_max	0.7311

Training Details

Training Dataset

sentence-transformers/wikipedia-en-sentences

Dataset: sentence-transformers/wikipedia-en-sentences at 4a0972d
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: 1024 elements

	sentence	label
type	string	list
details	min: 4 tokens mean: 12.24 tokens max: 52 tokens	size: 1024 elements

Samples:

sentence	label
`A person on a horse jumps over a broken down airplane.`	`[-0.5300068259239197, 0.07807248830795288, 0.304331511259079, 0.3473575711250305, 0.3993019461631775, ...]`
`Children smiling and waving at camera`	`[-0.3918086886405945, 0.514893114566803, 0.38178062438964844, -0.29475438594818115, -0.07984668761491776, ...]`
`A boy is jumping on skateboard in the middle of a red bridge.`	`[-0.7029106020927429, 0.08336036652326584, 0.7830113768577576, -0.7898964285850525, 0.27573251724243164, ...]`

Loss: MSELoss

Evaluation Dataset

sentence-transformers/wikipedia-en-sentences

Dataset: sentence-transformers/wikipedia-en-sentences at 4a0972d
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: 1024 elements

	sentence	label
type	string	list
details	min: 5 tokens mean: 13.23 tokens max: 57 tokens	size: 1024 elements

Samples:

sentence	label
`Two women are embracing while holding to go packages.`	`[-0.5707114338874817, -0.5041555762290955, -1.3100334405899048, 0.5848354697227478, -0.3452240526676178, ...]`
`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.`	`[-0.4810343384742737, 0.034435614943504333, -0.669406533241272, -0.16233624517917633, 0.5214978456497192, ...]`
`A man selling donuts to a customer during a world exhibition event held in the city of Angeles`	`[-0.2572114169597626, 0.19592943787574768, -0.6243088841438293, -0.4749126136302948, -0.6737443804740906, ...]`

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
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
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
batch_sampler: batch_sampler
multi_dataset_batch_sampler: proportional

Training Logs

Epoch	Step	Training Loss	loss	negative_mse	sts-dev_spearman_cosine	sts-test_spearman_cosine
0.1279	100	0.4302	-	-	-	-
0.2558	200	0.2398	-	-	-	-
0.3836	300	0.1918	-	-	-	-
0.5115	400	0.1683	-	-	-	-
0.6394	500	0.1539	0.2155	-21.5451	0.7347	-
0.7673	600	0.1456	-	-	-	-
0.8951	700	0.1393	-	-	-	-
1.0	782	-	-	-	-	0.7311

The bold row denotes the saved checkpoint.

Framework Versions

Python: 3.10.6
Sentence Transformers: 3.0.0
Transformers: 4.41.1
PyTorch: 2.3.0+cu121
Accelerate: 0.30.1
Datasets: 2.19.1
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",
}

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",
}