metadata

base_model: sentence-transformers/all-MiniLM-L6-v2
datasets:
  - sentence-transformers/stsb
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:101
  - loss:CoSENTLoss
widget:
  - source_sentence: The man is slicing a potato.
    sentences:
      - A woman is slicing carrot.
      - Two women are singing.
      - A man is slicing potato.
  - source_sentence: A girl is playing a flute.
    sentences:
      - A woman stirs eggs in a bowl.
      - A girl plays a wind instrument.
      - A man is turning over tables in anger.
  - source_sentence: People are playing baseball.
    sentences:
      - The cricket player hit the ball.
      - A man breaks a stick.
      - A woman is pouring a yellow mixture on a frying pan.
  - source_sentence: A woman and man are riding in a car.
    sentences:
      - A woman driving a car is talking to the man seated beside her.
      - A woman is placing skewered food onto a cooker.
      - The man and woman are walking.
  - source_sentence: A cat is on a robot.
    sentences:
      - A man is eating bread.
      - A woman is pouring eyes into a bowl.
      - A boy sits on a bed, sings and plays a guitar.
model-index:
  - name: SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2
    results:
      - task:
          type: semantic-similarity
          name: Semantic Similarity
        dataset:
          name: sts dev
          type: sts-dev
        metrics:
          - type: pearson_cosine
            value: 0.9186522039312566
            name: Pearson Cosine
          - type: spearman_cosine
            value: 0.9276278198564623
            name: Spearman Cosine
          - type: pearson_manhattan
            value: 0.8991493568260668
            name: Pearson Manhattan
          - type: spearman_manhattan
            value: 0.9320766471557739
            name: Spearman Manhattan
          - type: pearson_euclidean
            value: 0.9014580823459483
            name: Pearson Euclidean
          - type: spearman_euclidean
            value: 0.9289530024562572
            name: Spearman Euclidean
          - type: pearson_dot
            value: 0.8789190604301875
            name: Pearson Dot
          - type: spearman_dot
            value: 0.8957287815613981
            name: Spearman Dot
          - type: pearson_max
            value: 0.9186522039312566
            name: Pearson Max
          - type: spearman_max
            value: 0.9320766471557739
            name: Spearman Max

SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2

This is a sentence-transformers model finetuned from sentence-transformers/all-MiniLM-L6-v2. It maps sentences & paragraphs to a 384-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: sentence-transformers/all-MiniLM-L6-v2
Maximum Sequence Length: 512 tokens
Output Dimensionality: 384 tokens
Similarity Function: Cosine Similarity
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': 384, '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("Husain/ramdam_fingerprint_embedding_model")
# Run inference
sentences = [
    'A cat is on a robot.',
    'A man is eating bread.',
    'A woman is pouring eyes into a bowl.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# 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	0.9187
spearman_cosine	0.9276
pearson_manhattan	0.8991
spearman_manhattan	0.9321
pearson_euclidean	0.9015
spearman_euclidean	0.929
pearson_dot	0.8789
spearman_dot	0.8957
pearson_max	0.9187
spearman_max	0.9321

Training Details

Training Dataset

Unnamed Dataset

Size: 101 training samples
Columns: sentence1, sentence2, and score
Approximate statistics based on the first 101 samples:
sentence1 sentence2 score
type string string float
details
min: 7 tokens
mean: 9.44 tokens
max: 14 tokens

min: 3 tokens
mean: 9.46 tokens
max: 15 tokens

min: 0.1
mean: 0.66
max: 1.0

	sentence1	sentence2	score
type	string	string	float
details	min: 7 tokens mean: 9.44 tokens max: 14 tokens	min: 3 tokens mean: 9.46 tokens max: 15 tokens	min: 0.1 mean: 0.66 max: 1.0

Samples:

sentence1	sentence2	score
`A plane is taking off.`	`An air plane is taking off.`	`1.0`
`A man is playing a large flute.`	`A man is playing a flute.`	`0.76`
`A man is spreading shreded cheese on a pizza.`	`A man is spreading shredded cheese on an uncooked pizza.`	`0.76`

Loss: CoSENTLoss with these parameters:

{
    "scale": 20.0,
    "similarity_fct": "pairwise_cos_sim"
}

Evaluation Dataset

stsb

Dataset: stsb at ab7a5ac
Size: 1,500 evaluation samples
Columns: sentence1, sentence2, and score
Approximate statistics based on the first 1000 samples:
sentence1 sentence2 score
type string string float
details
min: 6 tokens
mean: 9.35 tokens
max: 13 tokens

min: 7 tokens
mean: 9.9 tokens
max: 16 tokens

min: 0.0
mean: 0.39
max: 1.0

	sentence1	sentence2	score
type	string	string	float
details	min: 6 tokens mean: 9.35 tokens max: 13 tokens	min: 7 tokens mean: 9.9 tokens max: 16 tokens	min: 0.0 mean: 0.39 max: 1.0

Samples:

sentence1	sentence2	score
`A woman is riding on a horse.`	`A man is turning over tables in anger.`	`0.0`
`A man is screwing wood to a wall.`	`A man is giving a woman a massage.`	`0.04`
`A girl is playing a flute.`	`A girl plays a wind instrument.`	`0.64`

Loss: CoSENTLoss with these parameters:

{
    "scale": 20.0,
    "similarity_fct": "pairwise_cos_sim"
}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
learning_rate: 2e-05
num_train_epochs: 10
warmup_ratio: 0.1
save_only_model: True
seed: 33
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: 8
per_device_eval_batch_size: 8
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: 2e-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: 10
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: True
restore_callback_states_from_checkpoint: False
no_cuda: False
use_cpu: False
use_mps_device: False
seed: 33
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
eval_on_start: False
eval_use_gather_object: False
batch_sampler: batch_sampler
multi_dataset_batch_sampler: proportional

Training Logs

Epoch	Step	loss	sts-dev_spearman_cosine
0.1538	2	4.4641	0.9366
0.3077	4	4.4652	0.9366
0.4615	6	4.4719	0.9366
0.6154	8	4.4903	0.9366
0.7692	10	4.5264	0.9373
0.9231	12	4.5954	0.9339
1.0769	14	4.6832	0.9328
1.2308	16	4.7534	0.9289
1.3846	18	4.8155	0.9281
1.5385	20	4.8788	0.9269
1.6923	22	4.9350	0.9272
1.8462	24	4.9789	0.9239
2.0	26	5.0132	0.9230
2.1538	28	5.0636	0.9237
2.3077	30	5.1068	0.9202
2.4615	32	5.1460	0.9172
2.6154	34	5.1602	0.9164
2.7692	36	5.1493	0.9210
2.9231	38	5.1399	0.9200
3.0769	40	5.1342	0.9235
3.2308	42	5.1413	0.9258
3.3846	44	5.1440	0.9271
3.5385	46	5.1583	0.9311
3.6923	48	5.1664	0.9293
3.8462	50	5.1682	0.9293
4.0	52	5.1617	0.9293
4.1538	54	5.1543	0.9293
4.3077	56	5.1480	0.9293
4.4615	58	5.1428	0.9291
4.6154	60	5.1292	0.9298
4.7692	62	5.1271	0.9276
4.9231	64	5.1133	0.9276
5.0769	66	5.0928	0.9270
5.2308	68	5.0874	0.9270
5.3846	70	5.0755	0.9270
5.5385	72	5.0665	0.9270
5.6923	74	5.0676	0.9293
5.8462	76	5.0747	0.9293
6.0	78	5.0647	0.9295
6.1538	80	5.0763	0.9273
6.3077	82	5.0832	0.9272
6.4615	84	5.0750	0.9289
6.6154	86	5.0547	0.9289
6.7692	88	5.0350	0.9308
6.9231	90	5.0221	0.9308
7.0769	92	5.0107	0.9308
7.2308	94	4.9967	0.9297
7.3846	96	4.9983	0.9297
7.5385	98	5.0026	0.9277
7.6923	100	5.0095	0.9277
7.8462	102	5.0102	0.9277
8.0	104	5.0055	0.9271
8.1538	106	5.0031	0.9271
8.3077	108	4.9976	0.9271
8.4615	110	4.9941	0.9271
8.6154	112	4.9856	0.9276
8.7692	114	4.9821	0.9276
8.9231	116	4.9782	0.9276
9.0769	118	4.9706	0.9276
9.2308	120	4.9646	0.9276
9.3846	122	4.9584	0.9276
9.5385	124	4.9537	0.9276
9.6923	126	4.9499	0.9276
9.8462	128	4.9485	0.9276
10.0	130	4.9463	0.9276

The bold row denotes the saved checkpoint.

Framework Versions

Python: 3.8.10
Sentence Transformers: 3.1.0
Transformers: 4.44.2
PyTorch: 2.3.1+cu121
Accelerate: 0.34.2
Datasets: 3.0.0
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",
}

CoSENTLoss

@online{kexuefm-8847,
    title={CoSENT: A more efficient sentence vector scheme than Sentence-BERT},
    author={Su Jianlin},
    year={2022},
    month={Jan},
    url={https://kexue.fm/archives/8847},
}