metadata

language: []
library_name: sentence-transformers
tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - dataset_size:100K<n<1M
  - loss:CoSENTLoss
base_model: sentence-transformers/all-MiniLM-L6-v2
metrics:
  - pearson_cosine
  - spearman_cosine
  - pearson_manhattan
  - spearman_manhattan
  - pearson_euclidean
  - spearman_euclidean
  - pearson_dot
  - spearman_dot
  - pearson_max
  - spearman_max
widget:
  - source_sentence: Frequent headaches and muscle soreness are a result of my insomnia.
    sentences:
      - >-
        My frequent headaches and muscle soreness are a direct result of my
        insomnia.
      - >-
        A manic episode often prevents me from sitting still or relaxing as I
        constantly need to be on the move.
      - >-
        The fear of being away from familiar places during a panic attack is why
        I have refused job opportunities with travel obligations.
  - source_sentence: My insomnia results in frequent headaches and muscle soreness for me.
    sentences:
      - Due to my insomnia, I have frequent headaches and muscle soreness.
      - >-
        Thoughts of life not being worth living and feelings of hopelessness
        create a difficult challenge for me.
      - >-
        The fear of being away from familiar places during a panic attack is why
        I have refused job opportunities with travel obligations.
  - source_sentence: Faced with a snake, fear takes over and I stay frozen until it passes.
    sentences:
      - >-
        Whenever I encounter a snake, I freeze in fear and cannot move until it
        is gone.
      - >-
        Due to a sense of unworthiness of happiness, I struggle to enjoy
        activities that were once my favorites.
      - >-
        The fear of being away from familiar places during a panic attack is why
        I have refused job opportunities with travel obligations.
  - source_sentence: The idea of overdosing on medication crosses my mind when overwhelmed.
    sentences:
      - >-
        Thoughts of overdosing on medication often occur to me when I'm
        overwhelmed.
      - >-
        I, almost like being stuck in a loop, repeat certain actions or words
        without any clear purpose at times.
      - >-
        The fear of being away from familiar places during a panic attack is why
        I have refused job opportunities with travel obligations.
  - source_sentence: Insomnia has led me to experience frequent headaches and muscle soreness.
    sentences:
      - >-
        My insomnia has caused me to experience frequent headaches and muscle
        soreness.
      - >-
        I struggle with distinguishing between reality and illusions when I feel
        detached from reality at times.
      - >-
        The fear of being away from familiar places during a panic attack is why
        I have refused job opportunities with travel obligations.
pipeline_tag: sentence-similarity
model-index:
  - name: SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2
    results:
      - task:
          type: semantic-similarity
          name: Semantic Similarity
        dataset:
          name: FT label aug
          type: FT_label_aug
        metrics:
          - type: pearson_cosine
            value: 0.42561450628852554
            name: Pearson Cosine
          - type: spearman_cosine
            value: 0.23253817395631948
            name: Spearman Cosine
          - type: pearson_manhattan
            value: 0.5095430319125491
            name: Pearson Manhattan
          - type: spearman_manhattan
            value: 0.23187290173483613
            name: Spearman Manhattan
          - type: pearson_euclidean
            value: 0.5153981915417447
            name: Pearson Euclidean
          - type: spearman_euclidean
            value: 0.232538168642362
            name: Spearman Euclidean
          - type: pearson_dot
            value: 0.4256145064012167
            name: Pearson Dot
          - type: spearman_dot
            value: 0.23253817993475548
            name: Spearman Dot
          - type: pearson_max
            value: 0.5153981915417447
            name: Pearson Max
          - type: spearman_max
            value: 0.23253817993475548
            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: 256 tokens
Output Dimensionality: 384 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: 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})
  (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("Hgkang00/FT-label-aug-consent-10")
# Run inference
sentences = [
    'Insomnia has led me to experience frequent headaches and muscle soreness.',
    'My insomnia has caused me to experience frequent headaches and muscle soreness.',
    'I struggle with distinguishing between reality and illusions when I feel detached from reality at times.',
]
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: FT_label_aug
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.4256
spearman_cosine	0.2325
pearson_manhattan	0.5095
spearman_manhattan	0.2319
pearson_euclidean	0.5154
spearman_euclidean	0.2325
pearson_dot	0.4256
spearman_dot	0.2325
pearson_max	0.5154
spearman_max	0.2325

Training Details

Training Dataset

Unnamed Dataset

Size: 133,800 training samples
Columns: sentence1, sentence2, and score
Approximate statistics based on the first 1000 samples:
sentence1 sentence2 score
type string string float
details
min: 11 tokens
mean: 31.63 tokens
max: 63 tokens

min: 14 tokens
mean: 25.22 tokens
max: 41 tokens

min: -1.0
mean: -0.92
max: 1.0

	sentence1	sentence2	score
type	string	string	float
details	min: 11 tokens mean: 31.63 tokens max: 63 tokens	min: 14 tokens mean: 25.22 tokens max: 41 tokens	min: -1.0 mean: -0.92 max: 1.0

Samples:

sentence1	sentence2	score
`Presence of one or more of the following intrusion symptoms associated with the traumatic event: recurrent distressing memories, dreams, flashbacks, psychological distress, or physiological reactions to cues of the traumatic event.`	`I avoid making phone calls, even to close friends or family, because I'm afraid of saying something wrong or sounding awkward.`	`0.0`
`The phobic object or situation almost always provokes immediate fear or anxiety.`	`I find it hard to stick to a consistent eating schedule, sometimes going days without feeling the need to eat at all.`	`-1.0`
`The fear or anxiety is out of proportion to the actual danger posed by the specific object or situation and to the sociocultural context.`	`I have difficulty going to places where I feel there are no immediate exits, such as cinemas or auditoriums, as the fear of being stuck or unable to escape escalates my anxiety.`	`-1.0`

Loss: CoSENTLoss with these parameters:

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

Evaluation Dataset

Unnamed Dataset

Size: 104,225 evaluation samples
Columns: sentence1, sentence2, and score
Approximate statistics based on the first 1000 samples:
sentence1 sentence2 score
type string string float
details
min: 11 tokens
mean: 31.24 tokens
max: 63 tokens

min: 15 tokens
mean: 24.86 tokens
max: 41 tokens

min: -1.0
mean: -0.93
max: 1.0

	sentence1	sentence2	score
type	string	string	float
details	min: 11 tokens mean: 31.24 tokens max: 63 tokens	min: 15 tokens mean: 24.86 tokens max: 41 tokens	min: -1.0 mean: -0.93 max: 1.0

Samples:

sentence1	sentence2	score
`Excessive anxiety and worry occurring more days than not for at least 6 months, about a number of events or activities such as work or school performance.`	`Simple activities like going for a walk or doing household chores feel like daunting tasks due to my low energy levels.`	`-1.0`
`The individual fears acting in a way or showing anxiety symptoms that will be negatively evaluated, leading to humiliation, embarrassment, rejection, or offense to others.`	`I often find myself mindlessly snacking throughout the day due to changes in my appetite.`	`-1.0`
`Persistent avoidance of stimuli associated with the trauma, evidenced by avoiding distressing memories, thoughts, or feelings, or external reminders of the event.`	`Simple activities like going for a walk or doing household chores feel like daunting tasks due to my low energy levels.`	`-1.0`

Loss: CoSENTLoss with these parameters:

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

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: epoch
per_device_train_batch_size: 256
per_device_eval_batch_size: 128
num_train_epochs: 10
warmup_ratio: 0.1
batch_sampler: no_duplicates

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: epoch
prediction_loss_only: True
per_device_train_batch_size: 256
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
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: 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: 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: proportional

Training Logs

Epoch	Step	Training Loss	loss	FT_label_aug_spearman_cosine
1.0	523	7.773	-	-
2.0	1046	0.0004	-	-
2.9828	1560	-	11.8818	0.2184
1.0172	1569	0.1169	-	-
2.0172	2092	5.4076	-	-
3.0172	2615	0.0002	-	-
3.9828	3120	-	11.8669	0.2054
2.0344	3138	0.1571	-	-
3.0344	3661	4.0179	-	-
4.0344	4184	0.0001	-	-
4.9828	4680	-	12.8814	0.2291
3.0516	4707	0.1592	-	-
4.0516	5230	2.835	13.5336	0.2325

Framework Versions

Python: 3.10.12
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",
}

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},
}