SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2

This is a sentence-transformers model finetuned from sentence-transformers/all-MiniLM-L6-v2 on the synthetic-esco-skill-sentences dataset. It maps inputs to a 384-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, 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 dimensions
Similarity Function: Cosine Similarity
Supported Modality: Text
Training Dataset:
- synthetic-esco-skill-sentences
Language: en

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({'module_input_name': 'sentence_embedding', 'module_output_name': 'sentence_embedding'})
)

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 = [
    'looking for experienced animal hunters to join our team',
    'animal hunting',
    'electrical engineering',
]
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,  0.8278, -0.1120],
#         [ 0.8278,  1.0000, -0.1666],
#         [-0.1120, -0.1666,  1.0000]])

Training Details

Training Dataset

synthetic-esco-skill-sentences

Dataset: synthetic-esco-skill-sentences at 09fc487
Size: 110,608 training samples
Columns: anchor and positive
Approximate statistics based on the first 100 samples:
anchor positive
type string string

modality text text
details
min: 11 tokens
mean: 19.69 tokens
max: 30 tokens

min: 3 tokens
mean: 6.53 tokens
max: 15 tokens

	anchor	positive
type	string	string
modality	text	text
details	min: 11 tokens mean: 19.69 tokens max: 30 tokens	min: 3 tokens mean: 6.53 tokens max: 15 tokens

Samples:

anchor	positive
`Applicants should be skilled in examining legislative drafts for legal compliance and accuracy.`	`examine legislative drafts`
`Candidate must have knowledge in photographic film processing, including monitoring chemical baths.`	`monitor film development baths`
`we are looking for an experienced meat grinder to join our team`	`grind meat`

Loss: MultipleNegativesRankingLoss with these parameters:

{
    "scale": 20.0,
    "similarity_fct": "cos_sim",
    "gather_across_devices": false,
    "directions": [
        "query_to_doc"
    ],
    "partition_mode": "joint",
    "hardness_mode": null,
    "hardness_strength": 0.0
}

Evaluation Dataset

synthetic-esco-skill-sentences

Dataset: synthetic-esco-skill-sentences at 09fc487
Size: 27,652 evaluation samples
Columns: anchor and positive
Approximate statistics based on the first 100 samples:
anchor positive
type string string

modality text text
details
min: 11 tokens
mean: 19.55 tokens
max: 34 tokens

min: 3 tokens
mean: 5.96 tokens
max: 10 tokens

	anchor	positive
type	string	string
modality	text	text
details	min: 11 tokens mean: 19.55 tokens max: 34 tokens	min: 3 tokens mean: 5.96 tokens max: 10 tokens

Samples:

anchor	positive
`the position requires attention to detail and strong organizational skills in order to maintain accurate records of security incidents.`	`document security incidents in the store`
`We need someone with experience in export logistics to lead our transportation team`	`coordinate export transportation activities`
`The ideal candidate will have experience in developing food policy that meets the social objectives of our company.`	`develop food policy`

Loss: MultipleNegativesRankingLoss with these parameters:

{
    "scale": 20.0,
    "similarity_fct": "cos_sim",
    "gather_across_devices": false,
    "directions": [
        "query_to_doc"
    ],
    "partition_mode": "joint",
    "hardness_mode": null,
    "hardness_strength": 0.0
}

Training Hyperparameters

Non-Default Hyperparameters

per_device_train_batch_size: 128
num_train_epochs: 50
learning_rate: 0.0001
weight_decay: 0.01
per_device_eval_batch_size: 128
dataloader_pin_memory: False

All Hyperparameters

Click to expand

per_device_train_batch_size: 128
num_train_epochs: 50
max_steps: -1
learning_rate: 0.0001
lr_scheduler_type: linear
lr_scheduler_kwargs: None
warmup_steps: 0
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: 128
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: False
dataloader_persistent_workers: False
dataloader_prefetch_factor: None
dataloader_multiprocessing_context: None
dataloader_in_order: True
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: None
fsdp_config: None
deepspeed: None
debug: []
skip_memory_metrics: True
do_predict: False
resume_from_checkpoint: None
local_rank: -1
prompts: None
batch_sampler: batch_sampler
multi_dataset_batch_sampler: proportional
router_mapping: {}
learning_rate_mapping: {}
warmup_ratio: None

Training Logs

Epoch	Step	Training Loss	Validation Loss
0.1156	100	-	0.2757
0.2312	200	-	0.2636
0.3468	300	-	0.2548
0.4624	400	-	0.2484
0.5780	500	0.2929	0.2452
0.6936	600	-	0.2387
0.8092	700	-	0.2291
0.9249	800	-	0.2355
1.0405	900	-	0.2288
1.1561	1000	0.2378	0.2275
1.2717	1100	-	0.2263
1.3873	1200	-	0.2282
1.5029	1300	-	0.2228
1.6185	1400	-	0.2199
1.7341	1500	0.1797	0.2235
1.8497	1600	-	0.2181
1.9653	1700	-	0.2214
2.0809	1800	-	0.2164
2.1965	1900	-	0.2150
2.3121	2000	0.1466	0.2133
2.4277	2100	-	0.2122
2.5434	2200	-	0.2117
2.6590	2300	-	0.2118
2.7746	2400	-	0.2113
2.8902	2500	0.1352	0.2097
3.0058	2600	-	0.2075
3.1214	2700	-	0.2110
3.2370	2800	-	0.2114
3.3526	2900	-	0.2170
3.4682	3000	0.1012	0.2143
3.5838	3100	-	0.2110

Training Time

Training: 20.9 minutes
Evaluation: 13.3 minutes
Total: 34.2 minutes

Framework Versions

Python: 3.14.7
Sentence Transformers: 6.2.0
Transformers: 5.22.0
PyTorch: 2.15.0
Accelerate: 1.15.0
Datasets: 5.1.0
Tokenizers: 0.23.3

Additional Resources

Training and Finetuning Embedding Models with Sentence Transformers: the end-to-end guide for training or finetuning Sentence Transformer models.
Introduction to Matryoshka Embedding Models: variable-size embeddings that can be truncated with minimal quality loss.
Binary and Scalar Embedding Quantization for Significantly Faster & Cheaper Retrieval: post-training compression of embedding vectors.
Multimodal Embedding & Reranker Models with Sentence Transformers: use text, image, audio, and video models through the same API.
Training and Finetuning Multimodal Embedding & Reranker Models with Sentence Transformers: train multimodal embedding models, with a Visual Document Retrieval walkthrough.

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",
}

MultipleNegativesRankingLoss

@misc{oord2019representationlearningcontrastivepredictive,
      title={Representation Learning with Contrastive Predictive Coding},
      author={Aaron van den Oord and Yazhe Li and Oriol Vinyals},
      year={2019},
      eprint={1807.03748},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/1807.03748},
}