sentence-roberta-small / README.md

djsull

Update README.md

63c9f5e verified 10 months ago

preview code

raw

history blame contribute delete

16.5 kB

metadata

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
widget:
  - source_sentence: 백마를 이끄는 여자
    sentences:
      - 갈색 말을 타고 있는 여자
      - 남자와 여자가 키스하고 있다.
      - 남자가 칼로 물병을 썰고 있다
  - source_sentence: 꽤 괜찮은데
    sentences:
      - 아주 좋아요.
      - 개가 옷을 입고 있다.
      - 아무도 무대에 서지 않는다.
  - source_sentence: 지루하군요.
    sentences:
      - 힘드네요! 정말 힘드네요!
      - 여자는 아이를 돕는다.
      - 사람들이 손을 내밀고 있다
  - source_sentence: 인간의 지위
    sentences:
      - 인간의 지위.
      - 그것은 비열하지 않다.
      - 아무도 해고당하지 않는다.
  - source_sentence: 인간의 지적
    sentences:
      - 인간 관찰
      - 사람들이 안에 서 있다
      - 아무도 앉아 있지 않다
pipeline_tag: sentence-similarity
model-index:
  - name: SentenceTransformer based on klue/roberta-small
    results:
      - task:
          type: semantic-similarity
          name: Semantic Similarity
        dataset:
          name: sts dev
          type: sts-dev
        metrics:
          - type: pearson_cosine
            value: 0.848109514939322
            name: Pearson Cosine
          - type: spearman_cosine
            value: 0.8469617889194193
            name: Spearman Cosine
          - type: pearson_manhattan
            value: 0.8290541524988974
            name: Pearson Manhattan
          - type: spearman_manhattan
            value: 0.832916353112548
            name: Spearman Manhattan
          - type: pearson_euclidean
            value: 0.8296914939989355
            name: Pearson Euclidean
          - type: spearman_euclidean
            value: 0.8335696459808043
            name: Spearman Euclidean
          - type: pearson_dot
            value: 0.7961861998493428
            name: Pearson Dot
          - type: spearman_dot
            value: 0.7996870460025013
            name: Spearman Dot
          - type: pearson_max
            value: 0.848109514939322
            name: Pearson Max
          - type: spearman_max
            value: 0.8469617889194193
            name: Spearman Max

Model Details

Model Description

Model Type: Sentence Transformer
Maximum Sequence Length: 256 tokens
Output Dimensionality: 768 tokens
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': 256, 'do_lower_case': False}) with Transformer model: RobertaModel 
  (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("sentence_transformers_model_id")
# Run inference
sentences = [
    '인간의 지적',
    '인간 관찰',
    '사람들이 안에 서 있다',
]
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-dev
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.8481
spearman_cosine	0.847
pearson_manhattan	0.8291
spearman_manhattan	0.8329
pearson_euclidean	0.8297
spearman_euclidean	0.8336
pearson_dot	0.7962
spearman_dot	0.7997
pearson_max	0.8481
spearman_max	0.847

Training Details

Training Datasets

Unnamed Dataset

Size: 568,640 training samples
Columns: sentence_0, sentence_1, and sentence_2

Approximate statistics based on the first 1000 samples:

	sentence_0	sentence_1	sentence_2
type	string	string	string
details	min: 4 tokens mean: 19.02 tokens max: 156 tokens	min: 4 tokens mean: 18.36 tokens max: 95 tokens	min: 4 tokens mean: 14.31 tokens max: 35 tokens

Samples:

sentence_0	sentence_1	sentence_2
`악기를 연주하는 사람.`	`여자 옆에서 백파이프를 연주하는 잘 차려입은 남자`	`노숙자가 잔돈을 구걸한다.`
`셔츠에 이벤트 번호를 새긴 남자들은 길을 걸어간다.`	`멘스 셔츠에 숫자가 적혀 있다.`	`남자들이 길에서 자고 있다.`
`군인들은 기지에서 함께 어울린다.`	`한 무리의 군인들이 그늘을 입고 방에 함께 앉아 있었고, 벽에 있는 작은 틈으로 빛이 최고조에 달했다.`	`한 무리의 민간인들이 적의 공격으로부터 움츠러든다.`

Loss: MatryoshkaLoss with these parameters:

{
    "loss": "MultipleNegativesRankingLoss",
    "matryoshka_dims": [
        768,
        256
    ],
    "matryoshka_weights": [
        1,
        1
    ],
    "n_dims_per_step": -1
}

Unnamed Dataset

Size: 5,749 training samples
Columns: sentence_0, sentence_1, and label
Approximate statistics based on the first 1000 samples:
sentence_0 sentence_1 label
type string string float
details
min: 5 tokens
mean: 17.15 tokens
max: 71 tokens

min: 4 tokens
mean: 16.86 tokens
max: 76 tokens

min: 0.0
mean: 0.54
max: 1.0

	sentence_0	sentence_1	label
type	string	string	float
details	min: 5 tokens mean: 17.15 tokens max: 71 tokens	min: 4 tokens mean: 16.86 tokens max: 76 tokens	min: 0.0 mean: 0.54 max: 1.0

Samples:

sentence_0	sentence_1	label
`남자가 기타를 치고 있다.`	`소뇌는 기타를 치고 있다.`	`0.72`
`고양이가 빨판을 핥고 있다.`	`한 여성이 오이를 자르고 있다.`	`0.0`
`누군가가 파워 드릴로 나무 조각에 구멍을 뚫는다.`	`한 남자가 나무 조각에 구멍을 뚫는다.`	`0.64`

Loss: MatryoshkaLoss with these parameters:

{
    "loss": "CosineSimilarityLoss",
    "matryoshka_dims": [
        768,
        256
    ],
    "matryoshka_weights": [
        1,
        1
    ],
    "n_dims_per_step": -1
}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
num_train_epochs: 5
batch_sampler: no_duplicates
multi_dataset_batch_sampler: round_robin

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
learning_rate: 5e-05
weight_decay: 0.0
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
max_grad_norm: 1
num_train_epochs: 5
max_steps: -1
lr_scheduler_type: linear
lr_scheduler_kwargs: {}
warmup_ratio: 0.0
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: False
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: 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: no_duplicates
multi_dataset_batch_sampler: round_robin

Training Logs

Epoch	Step	Training Loss	sts-dev_spearman_max
0.3477	500	0.931	-
0.6954	1000	0.7062	0.8313
1.0007	1439	-	0.8379
1.0424	1500	0.5893	-
1.3901	2000	0.3406	0.8343
1.7378	2500	0.2514	-
2.0007	2878	-	0.8450
2.0848	3000	0.2252	0.8470

Framework Versions

Python: 3.10.12
Sentence Transformers: 3.0.0
Transformers: 4.41.2
PyTorch: 2.3.0+cu121
Accelerate: 0.30.1
Datasets: 2.19.2
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",
}

MatryoshkaLoss

@misc{kusupati2024matryoshka,
    title={Matryoshka Representation Learning}, 
    author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
    year={2024},
    eprint={2205.13147},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}

MultipleNegativesRankingLoss

@misc{henderson2017efficient,
    title={Efficient Natural Language Response Suggestion for Smart Reply}, 
    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
    year={2017},
    eprint={1705.00652},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}