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 retrieval.

Model Details

Model Description

Model Type: Sentence Transformer
Base model: sentence-transformers/all-MiniLM-L6-v2
Maximum Sequence Length: 256 tokens
Output Dimensionality: 384 dimensions
Similarity Function: Cosine Similarity
Supported Modality: Text

Model Sources

Documentation: Sentence Transformers Documentation
Repository: Sentence Transformers on GitHub
Hugging Face: Sentence Transformers on Hugging Face

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'transformer_task': 'feature-extraction', 'modality_config': {'text': {'method': 'forward', 'method_output_name': 'last_hidden_state'}}, 'module_output_name': 'token_embeddings', 'architecture': 'BertModel'})
  (1): Pooling({'embedding_dimension': 384, 'pooling_mode': 'mean', '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.

python

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
    'What is 100 degrees Fahrenheit converted to Celsius?',
    'Architect a globally distributed microservices deployment platform enforcing zero-trust networking, canary releases with automated rollback based on multi-region SLOs, cross-cloud secrets rotation, and infrastructure cost allocation across AWS, Azure, and GCP.',
    'What is the default network port for HTTP?',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[ 1.0000, -0.2282,  0.9992],
#         [-0.2282,  1.0000, -0.2242],
#         [ 0.9992, -0.2242,  1.0000]])

Training Details

Training Dataset

Unnamed Dataset

Size: 871 training samples
Columns: sentence and label
Approximate statistics based on the first 871 samples:
sentence label
type string int
details
min: 6 tokens
mean: 34.77 tokens
max: 179 tokens
0: ~19.86%
1: ~28.47%
2: ~27.10%
3: ~24.57%

Samples:

sentence	label
`Write a discharge instruction template for patients recovering from total knee replacement surgery, including medication guidelines, physical therapy milestones, and red-flag symptoms.`	`1`
`Prove that alpha-beta pruning in game tree search returns identical minimax values as full search for deterministic two-player zero-sum games with perfect information.`	`3`
`Explain the zone of proximal development using a specific classroom learning scenario.`	`1`

Loss: BatchAllTripletLoss with these parameters:
json
1{ 2 "distance_metric": "euclidean_distance", 3 "margin": 5 4}

Training Hyperparameters

Non-Default Hyperparameters

per_device_train_batch_size: 32
num_train_epochs: 20
learning_rate: 2e-05
warmup_steps: 0.1
weight_decay: 0.01
batch_sampler: group_by_label

All Hyperparameters

Click to expand

per_device_train_batch_size: 32
num_train_epochs: 20
max_steps: -1
learning_rate: 2e-05
lr_scheduler_type: linear
lr_scheduler_kwargs: None
warmup_steps: 0.1
optim: adamw_torch_fused
optim_args: None
weight_decay: 0.01
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
optim_target_modules: None
gradient_accumulation_steps: 1
average_tokens_across_devices: True
max_grad_norm: 1.0
label_smoothing_factor: 0.0
bf16: False
fp16: False
bf16_full_eval: False
fp16_full_eval: False
tf32: None
gradient_checkpointing: False
gradient_checkpointing_kwargs: None
torch_compile: False
torch_compile_backend: None
torch_compile_mode: None
use_liger_kernel: False
liger_kernel_config: None
use_cache: False
neftune_noise_alpha: None
torch_empty_cache_steps: None
auto_find_batch_size: False
log_on_each_node: True
logging_nan_inf_filter: True
include_num_input_tokens_seen: no
log_level: passive
log_level_replica: warning
disable_tqdm: False
project: huggingface
trackio_space_id: None
trackio_bucket_id: None
trackio_static_space_id: None
per_device_eval_batch_size: 8
prediction_loss_only: True
eval_on_start: False
eval_do_concat_batches: True
eval_use_gather_object: False
eval_accumulation_steps: None
include_for_metrics: []
batch_eval_metrics: False
save_only_model: False
save_on_each_node: False
enable_jit_checkpoint: False
push_to_hub: False
hub_private_repo: None
hub_model_id: None
hub_strategy: every_save
hub_always_push: False
hub_revision: None
load_best_model_at_end: False
ignore_data_skip: False
restore_callback_states_from_checkpoint: False
full_determinism: False
seed: 42
data_seed: None
use_cpu: False
accelerator_config: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
parallelism_config: None
dataloader_drop_last: False
dataloader_num_workers: 0
dataloader_pin_memory: True
dataloader_persistent_workers: False
dataloader_prefetch_factor: None
remove_unused_columns: True
label_names: None
train_sampling_strategy: random
length_column_name: length
ddp_find_unused_parameters: None
ddp_bucket_cap_mb: None
ddp_broadcast_buffers: False
ddp_static_graph: None
ddp_backend: None
ddp_timeout: 1800
fsdp: []
fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
deepspeed: None
debug: []
skip_memory_metrics: True
do_predict: False
resume_from_checkpoint: None
warmup_ratio: None
local_rank: -1
prompts: None
batch_sampler: group_by_label
multi_dataset_batch_sampler: proportional
router_mapping: {}
learning_rate_mapping: {}

Training Logs

Epoch	Step	Training Loss
0.3704	10	4.9309
0.7407	20	4.9160
1.1111	30	4.8961
1.4815	40	4.7981
1.8519	50	4.6499
2.2222	60	4.4857
2.5926	70	4.2761
2.9630	80	4.1235
3.3333	90	4.0387
3.7037	100	3.9355
4.0741	110	3.9115
4.4444	120	3.8240
4.8148	130	3.7550
5.1852	140	3.7214
5.5556	150	3.6528
5.9259	160	3.6749
6.2963	170	3.6371
6.6667	180	3.6124
7.0370	190	3.5848
7.4074	200	3.5935
7.7778	210	3.5504
8.1481	220	3.5539
8.5185	230	3.5427
8.8889	240	3.5122
9.2593	250	3.4894
9.6296	260	3.5147
10.0	270	3.4909
10.3704	280	3.5146
10.7407	290	3.4988
11.1111	300	3.4591
11.4815	310	3.4997
11.8519	320	3.4847
12.2222	330	3.4704
12.5926	340	3.4826
12.9630	350	3.4474
13.3333	360	3.4807
13.7037	370	3.4772
14.0741	380	3.4373
14.4444	390	3.4747
14.8148	400	3.4712
15.1852	410	3.4310
15.5556	420	3.4720
15.9259	430	3.4650
16.2963	440	3.4394
16.6667	450	3.4700
17.0370	460	3.4330
17.4074	470	3.4684
17.7778	480	3.4706
18.1481	490	3.4456
18.5185	500	3.4681
18.8889	510	3.4560
19.2593	520	3.4415
19.6296	530	3.4683
20.0	540	3.4167

Training Time

Training: 12.6 minutes

Framework Versions

Python: 3.11.13
Sentence Transformers: 5.4.1
Transformers: 5.8.0
PyTorch: 2.11.0+cpu
Accelerate: 1.13.0
Datasets: 4.8.5
Tokenizers: 0.22.2

Citation

BibTeX

Sentence Transformers

bibtex

@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",
}

BatchAllTripletLoss