term-mapper

This is a sentence-transformers model finetuned from sentence-transformers/all-mpnet-base-v2. It maps sentences & paragraphs to a 768-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-mpnet-base-v2
Maximum Sequence Length: 384 tokens
Output Dimensionality: 768 dimensions
Similarity Function: Cosine Similarity
Language: en
License: apache-2.0

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': 384, 'do_lower_case': False, 'architecture': 'MPNetModel'})
  (1): Pooling({'word_embedding_dimension': 768, '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("sentence_transformers_model_id")
# Run inference
sentences = [
    'board cert agency code, Board Cert Agency Code',
    '2nd board cert',
    'comments',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[ 1.0000,  0.6759, -0.0045],
#         [ 0.6759,  1.0000,  0.0552],
#         [-0.0045,  0.0552,  1.0000]])

Training Details

Training Dataset

Unnamed Dataset

Size: 61,927 training samples
Columns: anchor and positive
Approximate statistics based on the first 1000 samples:
anchor positive
type string string
details
min: 9 tokens
mean: 10.39 tokens
max: 11 tokens

min: 3 tokens
mean: 6.42 tokens
max: 25 tokens

	anchor	positive
type	string	string
details	min: 9 tokens mean: 10.39 tokens max: 11 tokens	min: 3 tokens mean: 6.42 tokens max: 25 tokens

Samples:

anchor	positive
`accepting patients ind, Accepting Patients IND`	`primary spec accepting new patients for pcps and ob`
`accepting patients ind, Accepting Patients IND`	`accepting new patients (all practitioner types ongoing outpatient basis) (y n) (no blanks)`
`accepting patients ind, Accepting Patients IND`	`acc ind for pts`

Loss: MultipleNegativesRankingLoss with these parameters:

{
    "scale": 20.0,
    "similarity_fct": "cos_sim"
}

Evaluation Dataset

Unnamed Dataset

Size: 7,092 evaluation samples
Columns: anchor and positive
Approximate statistics based on the first 1000 samples:
anchor positive
type string string
details
min: 5 tokens
mean: 11.39 tokens
max: 19 tokens

min: 3 tokens
mean: 6.96 tokens
max: 23 tokens

	anchor	positive
type	string	string
details	min: 5 tokens mean: 11.39 tokens max: 19 tokens	min: 3 tokens mean: 6.96 tokens max: 23 tokens

Samples:

anchor	positive
`accepting patients ind, Accepting Patients IND`	`open close panel`
`accepting patients ind, Accepting Patients IND`	`panel status`
`accepting patients ind, Accepting Patients IND`	`commercial panel status`

Loss: MultipleNegativesRankingLoss with these parameters:

{
    "scale": 20.0,
    "similarity_fct": "cos_sim"
}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 32
per_device_eval_batch_size: 32
learning_rate: 2e-05
warmup_ratio: 0.1
fp16: True
load_best_model_at_end: True
batch_sampler: no_duplicates

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: steps
prediction_loss_only: True
per_device_train_batch_size: 32
per_device_eval_batch_size: 32
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: 2e-05
weight_decay: 0.0
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
max_grad_norm: 1.0
num_train_epochs: 3
max_steps: -1
lr_scheduler_type: linear
lr_scheduler_kwargs: {}
warmup_ratio: 0.1
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: True
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: True
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
hub_revision: None
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
liger_kernel_config: None
eval_use_gather_object: False
average_tokens_across_devices: False
prompts: None
batch_sampler: no_duplicates
multi_dataset_batch_sampler: proportional
router_mapping: {}
learning_rate_mapping: {}

Training Logs

Click to expand

Epoch	Step	Training Loss	Validation Loss
0.0258	50	0.8668	-
0.0517	100	0.7505	0.6548
0.0775	150	0.6506	-
0.1033	200	0.4672	0.4107
0.1291	250	0.403	-
0.1550	300	0.3284	0.2954
0.1808	350	0.3005	-
0.2066	400	0.2248	0.2149
0.2324	450	0.219	-
0.2583	500	0.1794	0.1685
0.2841	550	0.1441	-
0.3099	600	0.1522	0.1397
0.3357	650	0.1322	-
0.3616	700	0.1254	0.1283
0.3874	750	0.1194	-
0.4132	800	0.134	0.1140
0.4390	850	0.0932	-
0.4649	900	0.1025	0.0957
0.4907	950	0.1063	-
0.5165	1000	0.0956	0.0945
0.5424	1050	0.071	-
0.5682	1100	0.0727	0.0836
0.5940	1150	0.0895	-
0.6198	1200	0.0786	0.0750
0.6457	1250	0.0923	-
0.6715	1300	0.0905	0.0742
0.6973	1350	0.0522	-
0.7231	1400	0.0645	0.0693
0.7490	1450	0.0711	-
0.7748	1500	0.0655	0.0627
0.8006	1550	0.0532	-
0.8264	1600	0.0602	0.0615
0.8523	1650	0.0674	-
0.8781	1700	0.0537	0.0564
0.9039	1750	0.0578	-
0.9298	1800	0.0643	0.0533
0.9556	1850	0.0655	-
0.9814	1900	0.0562	0.0519
1.0072	1950	0.0538	-
1.0331	2000	0.043	0.0470
1.0589	2050	0.035	-
1.0847	2100	0.0412	0.0454
1.1105	2150	0.0362	-
1.1364	2200	0.0454	0.0449
1.1622	2250	0.0438	-
1.1880	2300	0.0453	0.0433
1.2138	2350	0.0298	-
1.2397	2400	0.0351	0.0444
1.2655	2450	0.0349	-
1.2913	2500	0.0391	0.0431
1.3171	2550	0.0404	-
1.3430	2600	0.0371	0.0423
1.3688	2650	0.0382	-
1.3946	2700	0.0325	0.0420
1.4205	2750	0.0394	-
1.4463	2800	0.0469	0.0421
1.4721	2850	0.0466	-
1.4979	2900	0.0374	0.0407
1.5238	2950	0.0321	-
1.5496	3000	0.022	0.0388
1.5754	3050	0.0229	-
1.6012	3100	0.0354	0.0367
1.6271	3150	0.0275	-
1.6529	3200	0.036	0.0358
1.6787	3250	0.0349	-
1.7045	3300	0.0359	0.0337
1.7304	3350	0.0386	-
1.7562	3400	0.029	0.0341
1.7820	3450	0.0348	-
1.8079	3500	0.0241	0.0342
1.8337	3550	0.0281	-
1.8595	3600	0.0239	0.0323
1.8853	3650	0.0281	-
1.9112	3700	0.0301	0.0323
1.9370	3750	0.0186	-
1.9628	3800	0.0246	0.0308
1.9886	3850	0.0315	-
2.0145	3900	0.0185	0.0302
2.0403	3950	0.0272	-
2.0661	4000	0.025	0.0304
2.0919	4050	0.0262	-
2.1178	4100	0.02	0.0306
2.1436	4150	0.0163	-
2.1694	4200	0.0301	0.0294
2.1952	4250	0.0176	-
2.2211	4300	0.0206	0.0297
2.2469	4350	0.0121	-
2.2727	4400	0.0206	0.0294
2.2986	4450	0.018	-
2.3244	4500	0.0178	0.0291
2.3502	4550	0.0153	-
2.3760	4600	0.0219	0.0288
2.4019	4650	0.0214	-
2.4277	4700	0.0212	0.0281
2.4535	4750	0.0183	-
2.4793	4800	0.0302	0.0280
2.5052	4850	0.0158	-
2.5310	4900	0.02	0.0274
2.5568	4950	0.0171	-
2.5826	5000	0.0275	0.0269
2.6085	5050	0.0193	-
2.6343	5100	0.0158	0.0269
2.6601	5150	0.0179	-
2.6860	5200	0.0214	0.0269
2.7118	5250	0.0225	-
2.7376	5300	0.0166	0.0264
2.7634	5350	0.0243	-
2.7893	5400	0.0154	0.0262
2.8151	5450	0.0245	-
2.8409	5500	0.0122	0.0261
2.8667	5550	0.0234	-
2.8926	5600	0.0217	0.0259
2.9184	5650	0.0166	-
2.9442	5700	0.0165	0.0258
2.9700	5750	0.0126	-
2.9959	5800	0.0201	0.0258

The bold row denotes the saved checkpoint.

Framework Versions

Python: 3.10.18
Sentence Transformers: 5.0.0
Transformers: 4.53.3
PyTorch: 2.7.1+cu126
Accelerate: 1.9.0
Datasets: 3.6.0
Tokenizers: 0.21.2

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{henderson2017efficient,
    title={Efficient Natural Language Response Suggestion for Smart Reply},
    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
    year={2017},
    eprint={1705.00652},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}