This is a sentence-transformers model trained on the train dataset. It maps sentences & paragraphs to a 512-dimensional dense vector space and can be used for retrieval.

Model Details

Model Description

Model Type: Sentence Transformer

Maximum Sequence Length: 32 tokens
Output Dimensionality: 512 dimensions
Similarity Function: Cosine Similarity
Supported Modality: Text
Training Dataset:
- train

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': 512, 'pooling_mode': 'mean', 'include_prompt': True})
)

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("blackmlengineer/occucoder-en-grande-v0")
# Run inference
sentences = [
    'Meat Wrapper',
    'Packers and Packagers, Hand',
    'Laundry and Dry-Cleaning Workers',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 512]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.6319, 0.2463],
#         [0.6319, 1.0000, 0.2226],
#         [0.2463, 0.2226, 1.0000]])

Training Details

Training Dataset

train

Dataset: train
Size: 991,580 training samples
Columns: anchor, positive, and negative

Approximate statistics based on the first 100 samples:

	anchor	positive	negative
type	string	string	string
modality	text	text	text
details	min: 3 tokens mean: 4.61 tokens max: 8 tokens	min: 3 tokens mean: 7.7 tokens max: 19 tokens	min: 3 tokens mean: 7.95 tokens max: 22 tokens

Samples:

anchor	positive	negative
`Personnel Psychologist`	`Industrial-Organizational Psychologists`	`Educational, Guidance, and Career Counselors and Advisors`
`Property Claims Adjuster`	`Claims Adjusters, Examiners, and Investigators`	`Administrative Law Judges, Adjudicators, and Hearing Officers`
`Assistant Professor`	`Art, Drama, and Music Teachers, Postsecondary`	`Area, Ethnic, and Cultural Studies Teachers, Postsecondary`

Loss: MultipleNegativesRankingLoss with these parameters:
json
1{ 2 "scale": 20.0, 3 "similarity_fct": "cos_sim", 4 "gather_across_devices": false, 5 "directions": [ 6 "query_to_doc" 7 ], 8 "partition_mode": "joint", 9 "hardness_mode": null, 10 "hardness_strength": 0.0 11}

Training Hyperparameters

Non-Default Hyperparameters

per_device_train_batch_size: 128
num_train_epochs: 5

All Hyperparameters

Click to expand

per_device_train_batch_size: 128
num_train_epochs: 5
max_steps: -1
learning_rate: 5e-05
lr_scheduler_type: linear
lr_scheduler_kwargs: None
warmup_steps: 0
optim: adamw_torch_fused
optim_args: None
weight_decay: 0.0
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: None
fsdp_config: None
deepspeed: None
debug: []
skip_memory_metrics: True
do_predict: False
resume_from_checkpoint: None
warmup_ratio: None
local_rank: -1
prompts: None
batch_sampler: batch_sampler
multi_dataset_batch_sampler: proportional
router_mapping: {}
learning_rate_mapping: {}

Training Logs

Epoch	Step	Training Loss
0.0645	500	2.9603
0.1291	1000	2.2931
0.1936	1500	2.0092
0.2582	2000	1.8182
0.3227	2500	1.6973
0.3872	3000	1.5918
0.4518	3500	1.5185
0.5163	4000	1.4450
0.5809	4500	1.3923
0.6454	5000	1.3537
0.7100	5500	1.3151
0.7745	6000	1.2772
0.8390	6500	1.2525
0.9036	7000	1.2216
0.9681	7500	1.2021
1.0327	8000	1.1715
1.0972	8500	1.1504
1.1617	9000	1.1407
1.2263	9500	1.1144
1.2908	10000	1.1011
1.3554	10500	1.1053
1.4199	11000	1.0903
1.4844	11500	1.0781
1.5490	12000	1.0647
1.6135	12500	1.0498
1.6781	13000	1.0415
1.7426	13500	1.0409
1.8072	14000	1.0259
1.8717	14500	1.0176
1.9362	15000	1.0121
2.0008	15500	1.0083
2.0653	16000	0.9812
2.1299	16500	0.9868
2.1944	17000	0.9793
2.2589	17500	0.9746
2.3235	18000	0.9676
2.3880	18500	0.9722
2.4526	19000	0.9607
2.5171	19500	0.9592
2.5816	20000	0.9477
2.6462	20500	0.9533
2.7107	21000	0.9435
2.7753	21500	0.9427
2.8398	22000	0.9381
2.9044	22500	0.9363
2.9689	23000	0.9245
3.0334	23500	0.9229
3.0980	24000	0.9153
3.1625	24500	0.9124
3.2271	25000	0.9009
3.2916	25500	0.9079
3.3561	26000	0.9077
3.4207	26500	0.9045
3.4852	27000	0.9060
3.5498	27500	0.9050
3.6143	28000	0.8989
3.6788	28500	0.8969
3.7434	29000	0.8988
3.8079	29500	0.9005
3.8725	30000	0.8944
3.9370	30500	0.8931
4.0015	31000	0.8910
4.0661	31500	0.8828
4.1306	32000	0.8725
4.1952	32500	0.8805
4.2597	33000	0.8787
4.3243	33500	0.8796
4.3888	34000	0.8814
4.4533	34500	0.8719
4.5179	35000	0.8703
4.5824	35500	0.8724
4.6470	36000	0.8724
4.7115	36500	0.8751
4.7760	37000	0.8802
4.8406	37500	0.8737
4.9051	38000	0.8787
4.9697	38500	0.8747

Training Time

Training: 1.3 hours

Framework Versions

Python: 3.14.5
Sentence Transformers: 5.6.0
Transformers: 5.12.1
PyTorch: 2.12.0
Accelerate: 1.14.0
Datasets: 5.0.0
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",
}

MultipleNegativesRankingLoss

bibtex

@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},
}