SentenceTransformer based on flax-sentence-embeddings/all_datasets_v4_MiniLM-L6

This is a sentence-transformers model finetuned from flax-sentence-embeddings/all_datasets_v4_MiniLM-L6. 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: flax-sentence-embeddings/all_datasets_v4_MiniLM-L6
Maximum Sequence Length: 128 tokens
Output Dimensionality: 384 dimensions
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': 128, '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("Auto-opts/flax-TMNRLB_CVR")
# Run inference
sentences = [
    'vitamin B12 for nerve health',
    'The Role of Magnesium in Muscle and Nerve Function',
    'Emerging Patterns in Roblox Code Distribution Channels',
]
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]

Training Details

Training Dataset

Unnamed Dataset

Size: 439,290 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: 7.43 tokens
max: 15 tokens

min: 5 tokens
mean: 11.34 tokens
max: 34 tokens

min: 0.01
mean: 0.94
max: 1.0

	sentence_0	sentence_1	label
type	string	string	float
details	min: 5 tokens mean: 7.43 tokens max: 15 tokens	min: 5 tokens mean: 11.34 tokens max: 34 tokens	min: 0.01 mean: 0.94 max: 1.0

Samples:

sentence_0	sentence_1	label
`common UTI misconceptions`	`How Antibiotics Like Fosfomycin Target Infections`	`1.0`
`diuretics for swelling`	`Venous Insufficiency and Its Impact on Leg Swelling`	`1.0`
`pelvic floor exercises benefits`	`Testosterone Levels and Their Impact on Erectile Health`	`1.0`

Loss: main.DualThresholdEnforcedMNRL1

Training Hyperparameters

Non-Default Hyperparameters

per_device_train_batch_size: 90
per_device_eval_batch_size: 90
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: no
prediction_loss_only: True
per_device_train_batch_size: 90
per_device_eval_batch_size: 90
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: 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: None
hub_always_push: False
gradient_checkpointing: False
gradient_checkpointing_kwargs: None
include_inputs_for_metrics: False
include_for_metrics: []
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
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
use_liger_kernel: False
eval_use_gather_object: False
average_tokens_across_devices: False
prompts: None
batch_sampler: no_duplicates
multi_dataset_batch_sampler: round_robin

Training Logs

Epoch	Step	Training Loss
0.1024	500	2.4422
0.2049	1000	1.8481
0.3073	1500	1.5855
0.4098	2000	1.4325
0.5122	2500	1.332
0.6146	3000	1.2434
0.7171	3500	1.1842
0.8195	4000	1.1338
0.9219	4500	1.0779
1.0244	5000	1.0283
1.1268	5500	0.996
1.2293	6000	0.954
1.3317	6500	0.9362
1.4341	7000	0.895
1.5366	7500	0.8776
1.6390	8000	0.8624
1.7414	8500	0.8438
1.8439	9000	0.8158
1.9463	9500	0.7958
2.0488	10000	0.7779
2.1512	10500	0.754
2.2536	11000	0.7332
2.3561	11500	0.722
2.4585	12000	0.711
2.5610	12500	0.6945
2.6634	13000	0.6965
2.7658	13500	0.6834
2.8683	14000	0.6676
2.9707	14500	0.6635
3.0731	15000	0.6484
3.1756	15500	0.6282
3.2780	16000	0.6297
3.3805	16500	0.6241
3.4829	17000	0.6214
3.5853	17500	0.61
3.6878	18000	0.6106
3.7902	18500	0.6006
3.8926	19000	0.6062
3.9951	19500	0.6022
4.0975	20000	0.5808
4.2000	20500	0.5855
4.3024	21000	0.5852
4.4048	21500	0.5757
4.5073	22000	0.5768
4.6097	22500	0.5715
4.7121	23000	0.5764
4.8146	23500	0.5732
4.9170	24000	0.5777

Framework Versions

Python: 3.12.3
Sentence Transformers: 4.1.0
Transformers: 4.52.3
PyTorch: 2.6.0+cu124
Accelerate: 1.7.0
Datasets: 3.6.0
Tokenizers: 0.21.1

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",
}

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Safetensors

Model size

22.7M params

Tensor type

F32

Model tree for Auto-opts/flax-TMNRLB_CVR

Base model

flax-sentence-embeddings/all_datasets_v4_MiniLM-L6

Finetuned

(5)

this model