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-triple-2")
# Run inference
sentences = [
'Experience frequent headaches and muscle soreness due to my insomnia.',
'I experience frequent headaches and muscle soreness because of my insomnia.',
"The struggle to focus during the day is often due to feeling exhausted even after a full night's sleep.",
]
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
Triplet
- Dataset:
FT-triple
- Evaluated with
TripletEvaluator
Metric | Value |
---|---|
cosine_accuracy | 0.8093 |
dot_accuracy | 0.1907 |
manhattan_accuracy | 0.8104 |
euclidean_accuracy | 0.8093 |
max_accuracy | 0.8104 |
Training Details
Training Dataset
Unnamed Dataset
- Size: 52,000 training samples
- Columns:
anchor
,positive
, andnegative
- Approximate statistics based on the first 1000 samples:
anchor positive negative type string string string details - min: 29 tokens
- mean: 29.0 tokens
- max: 29 tokens
- min: 18 tokens
- mean: 23.16 tokens
- max: 29 tokens
- min: 13 tokens
- mean: 24.81 tokens
- max: 42 tokens
- Samples:
anchor positive negative Presence of delusions, hallucinations or disorganized speech, for a significant portion of time within a 1-month period
Even in the privacy of my room, I hear voices that tell me things that are not real frequently.
My lack of pleasure in things I once enjoyed has caused me to lose interest in hobbies or activities that used to bring me joy.
Presence of delusions, hallucinations or disorganized speech, for a significant portion of time within a 1-month period
It's common for me to hear things that are not real, even when I'm in my room by myself.
Starting multiple projects simultaneously during these episodes makes me feel like I can accomplish everything at once.
Presence of delusions, hallucinations or disorganized speech, for a significant portion of time within a 1-month period
Even in the privacy of my room, I hear voices that tell me things that are not real frequently.
Even after a full night's sleep, I struggle to get out of bed in the morning, feeling tired.
- Loss:
TripletLoss
with these parameters:{ "distance_metric": "TripletDistanceMetric.EUCLIDEAN", "triplet_margin": 5 }
Evaluation Dataset
Unnamed Dataset
- Size: 3,718 evaluation samples
- Columns:
anchor
,positive
, andnegative
- Approximate statistics based on the first 1000 samples:
anchor positive negative type string string string details - min: 18 tokens
- mean: 32.73 tokens
- max: 60 tokens
- min: 14 tokens
- mean: 22.72 tokens
- max: 35 tokens
- min: 14 tokens
- mean: 24.7 tokens
- max: 47 tokens
- Samples:
anchor positive negative Presence of delusions, hallucinations or disorganized speech, for a significant portion of time within a 1-month period
Observers in my vicinity have noted the escalation of my erratic and unpredictable behavior.
It's a challenge for me to seek assistance in public places, even when I clearly need help.
Presence of delusions, hallucinations or disorganized speech, for a significant portion of time within a 1-month period
There has been a growing awareness among those around me about my increasingly erratic and unpredictable behavior.
The difficulty of connecting with others on a deeper level stems from feeling like I've lost a part of myself due to the traumatic event.
Presence of delusions, hallucinations or disorganized speech, for a significant portion of time within a 1-month period
It has come to the attention of those around me that my behavior is becoming more erratic and unpredictable.
My thoughts exhibited a chaotic and disconnected pattern in that manic episode.
- Loss:
TripletLoss
with these parameters:{ "distance_metric": "TripletDistanceMetric.EUCLIDEAN", "triplet_margin": 5 }
Training Hyperparameters
Non-Default Hyperparameters
eval_strategy
: epochper_device_train_batch_size
: 128per_device_eval_batch_size
: 64num_train_epochs
: 2warmup_ratio
: 0.1
All Hyperparameters
Click to expand
overwrite_output_dir
: Falsedo_predict
: Falseeval_strategy
: epochprediction_loss_only
: Trueper_device_train_batch_size
: 128per_device_eval_batch_size
: 64per_gpu_train_batch_size
: Noneper_gpu_eval_batch_size
: Nonegradient_accumulation_steps
: 1eval_accumulation_steps
: Nonelearning_rate
: 5e-05weight_decay
: 0.0adam_beta1
: 0.9adam_beta2
: 0.999adam_epsilon
: 1e-08max_grad_norm
: 1.0num_train_epochs
: 2max_steps
: -1lr_scheduler_type
: linearlr_scheduler_kwargs
: {}warmup_ratio
: 0.1warmup_steps
: 0log_level
: passivelog_level_replica
: warninglog_on_each_node
: Truelogging_nan_inf_filter
: Truesave_safetensors
: Truesave_on_each_node
: Falsesave_only_model
: Falserestore_callback_states_from_checkpoint
: Falseno_cuda
: Falseuse_cpu
: Falseuse_mps_device
: Falseseed
: 42data_seed
: Nonejit_mode_eval
: Falseuse_ipex
: Falsebf16
: Falsefp16
: Falsefp16_opt_level
: O1half_precision_backend
: autobf16_full_eval
: Falsefp16_full_eval
: Falsetf32
: Nonelocal_rank
: 0ddp_backend
: Nonetpu_num_cores
: Nonetpu_metrics_debug
: Falsedebug
: []dataloader_drop_last
: Falsedataloader_num_workers
: 0dataloader_prefetch_factor
: Nonepast_index
: -1disable_tqdm
: Falseremove_unused_columns
: Truelabel_names
: Noneload_best_model_at_end
: Falseignore_data_skip
: Falsefsdp
: []fsdp_min_num_params
: 0fsdp_config
: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}fsdp_transformer_layer_cls_to_wrap
: Noneaccelerator_config
: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}deepspeed
: Nonelabel_smoothing_factor
: 0.0optim
: adamw_torchoptim_args
: Noneadafactor
: Falsegroup_by_length
: Falselength_column_name
: lengthddp_find_unused_parameters
: Noneddp_bucket_cap_mb
: Noneddp_broadcast_buffers
: Falsedataloader_pin_memory
: Truedataloader_persistent_workers
: Falseskip_memory_metrics
: Trueuse_legacy_prediction_loop
: Falsepush_to_hub
: Falseresume_from_checkpoint
: Nonehub_model_id
: Nonehub_strategy
: every_savehub_private_repo
: Falsehub_always_push
: Falsegradient_checkpointing
: Falsegradient_checkpointing_kwargs
: Noneinclude_inputs_for_metrics
: Falseeval_do_concat_batches
: Truefp16_backend
: autopush_to_hub_model_id
: Nonepush_to_hub_organization
: Nonemp_parameters
:auto_find_batch_size
: Falsefull_determinism
: Falsetorchdynamo
: Noneray_scope
: lastddp_timeout
: 1800torch_compile
: Falsetorch_compile_backend
: Nonetorch_compile_mode
: Nonedispatch_batches
: Nonesplit_batches
: Noneinclude_tokens_per_second
: Falseinclude_num_input_tokens_seen
: Falseneftune_noise_alpha
: Noneoptim_target_modules
: Nonebatch_eval_metrics
: Falsebatch_sampler
: batch_samplermulti_dataset_batch_sampler
: proportional
Training Logs
Epoch | Step | Training Loss | loss | FT-triple_max_accuracy |
---|---|---|---|---|
0.2015 | 82 | 4.5671 | - | - |
0.4029 | 164 | 4.0669 | - | - |
0.6044 | 246 | 3.9861 | - | - |
0.8059 | 328 | 3.9519 | - | - |
1.0 | 407 | - | 4.0778 | 0.8244 |
1.0074 | 410 | 3.9194 | - | - |
1.2088 | 492 | 3.8925 | - | - |
1.4103 | 574 | 3.8823 | - | - |
1.6118 | 656 | 3.8871 | - | - |
1.8133 | 738 | 3.8603 | - | - |
2.0 | 814 | - | 4.0806 | 0.8104 |
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",
}
TripletLoss
@misc{hermans2017defense,
title={In Defense of the Triplet Loss for Person Re-Identification},
author={Alexander Hermans and Lucas Beyer and Bastian Leibe},
year={2017},
eprint={1703.07737},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
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Model tree for Hgkang00/FT-triple-2
Base model
sentence-transformers/all-MiniLM-L6-v2
Finetuned
this model
Evaluation results
- Cosine Accuracy on FT tripleself-reported0.809
- Dot Accuracy on FT tripleself-reported0.191
- Manhattan Accuracy on FT tripleself-reported0.810
- Euclidean Accuracy on FT tripleself-reported0.809
- Max Accuracy on FT tripleself-reported0.810