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

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}) with Transformer model: 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.

python

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
    'A black dog is drinking next to a brown and white dog that is looking at an orange ball in the lake, whilst a horse and rider passes behind.',
    'There are two people running around a track in lane three and the one wearing a blue shirt with a green thing over the eyes is just barely ahead of the guy wearing an orange shirt and sunglasses.',
    'the guy is dead',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]

Evaluation

Metrics

Semantic Similarity

Datasets: sts-dev and sts-test
Evaluated with EmbeddingSimilarityEvaluator

Metric	sts-dev	sts-test
pearson_cosine	0.8659	0.8309
spearman_cosine	0.8685	0.8339

Knowledge Distillation

Evaluated with MSEEvaluator

Metric	Value
negative_mse	-0.0158

Training Details

Training Dataset

Unnamed Dataset

Size: 9,014,210 training samples
Columns: sentence and label
Approximate statistics based on the first 1000 samples:
sentence label
type string list
details
min: 4 tokens
mean: 12.24 tokens
max: 52 tokens
size: 768 elements

Samples:

sentence	label
`A person on a horse jumps over a broken down airplane.`	`[-0.030610017478466034, 0.11742044985294342, 0.031586047261953354, 0.01859636977314949, 0.016319412738084793, ...]`
`Children smiling and waving at camera`	`[-0.006198188289999962, -0.036625951528549194, -0.005352460313588381, -0.006725294981151819, 0.05185901001095772, ...]`
`A boy is jumping on skateboard in the middle of a red bridge.`	`[-0.01783316768705845, -0.05204763263463974, -0.003716366598382592, 0.0009472182719036937, 0.05223219841718674, ...]`

Loss: MSELoss

Evaluation Dataset

Unnamed Dataset

Size: 10,000 evaluation samples
Columns: sentence and label
Approximate statistics based on the first 1000 samples:
sentence label
type string list
details
min: 5 tokens
mean: 13.23 tokens
max: 57 tokens
size: 768 elements

Samples:

sentence	label
`Two women are embracing while holding to go packages.`	`[0.010130808688700199, 0.009573593735694885, -0.00034817546838894486, -0.0040625291876494884, 0.02026110142469406, ...]`
`Two young children in blue jerseys, one with the number 9 and one with the number 2 are standing on wooden steps in a bathroom and washing their hands in a sink.`	`[-0.033891696482896805, -0.04130887985229492, -0.006042165216058493, -0.02770376019179821, -0.0017171527724713087, ...]`
`A man selling donuts to a customer during a world exhibition event held in the city of Angeles`	`[0.0013940087519586086, -0.044612932950258255, -0.023834265768527985, 0.11863800883293152, -0.03907289728522301, ...]`

Loss: MSELoss

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 64
per_device_eval_batch_size: 64
learning_rate: 0.0001
num_train_epochs: 1
warmup_ratio: 0.1
fp16: True
load_best_model_at_end: True

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: steps
prediction_loss_only: True
per_device_train_batch_size: 64
per_device_eval_batch_size: 64
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: 0.0001
weight_decay: 0.0
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
max_grad_norm: 1.0
num_train_epochs: 1
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}
tp_size: 0
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: batch_sampler
multi_dataset_batch_sampler: proportional

Training Logs

Click to expand

Epoch	Step	Training Loss	Validation Loss	sts-dev_spearman_cosine	negative_mse	sts-test_spearman_cosine
-1	-1	-	-	0.6786	-0.2176	-
0.0071	1000	0.0016	-	-	-	-
0.0142	2000	0.001	-	-	-	-
0.0213	3000	0.0008	-	-	-	-
0.0284	4000	0.0007	-	-	-	-
0.0355	5000	0.0006	0.0006	0.8511	-0.0561	-
0.0426	6000	0.0006	-	-	-	-
0.0497	7000	0.0005	-	-	-	-
0.0568	8000	0.0005	-	-	-	-
0.0639	9000	0.0005	-	-	-	-
0.0710	10000	0.0004	0.0004	0.8624	-0.0361	-
0.0781	11000	0.0004	-	-	-	-
0.0852	12000	0.0004	-	-	-	-
0.0923	13000	0.0004	-	-	-	-
0.0994	14000	0.0004	-	-	-	-
0.1065	15000	0.0003	0.0003	0.8649	-0.0288	-
0.1136	16000	0.0003	-	-	-	-
0.1207	17000	0.0003	-	-	-	-
0.1278	18000	0.0003	-	-	-	-
0.1349	19000	0.0003	-	-	-	-
0.1420	20000	0.0003	0.0003	0.8663	-0.0252	-
0.1491	21000	0.0003	-	-	-	-
0.1562	22000	0.0003	-	-	-	-
0.1633	23000	0.0003	-	-	-	-
0.1704	24000	0.0003	-	-	-	-
0.1775	25000	0.0003	0.0002	0.8641	-0.0232	-
0.1846	26000	0.0003	-	-	-	-
0.1917	27000	0.0003	-	-	-	-
0.1988	28000	0.0003	-	-	-	-
0.2059	29000	0.0003	-	-	-	-
0.2130	30000	0.0003	0.0002	0.8641	-0.0219	-
0.2201	31000	0.0003	-	-	-	-
0.2272	32000	0.0003	-	-	-	-
0.2343	33000	0.0003	-	-	-	-
0.2414	34000	0.0003	-	-	-	-
0.2485	35000	0.0003	0.0002	0.8649	-0.0209	-
0.2556	36000	0.0003	-	-	-	-
0.2627	37000	0.0003	-	-	-	-
0.2698	38000	0.0003	-	-	-	-
0.2769	39000	0.0003	-	-	-	-
0.2840	40000	0.0003	0.0002	0.8648	-0.0202	-
0.2911	41000	0.0003	-	-	-	-
0.2982	42000	0.0002	-	-	-	-
0.3053	43000	0.0002	-	-	-	-
0.3124	44000	0.0002	-	-	-	-
0.3195	45000	0.0002	0.0002	0.8663	-0.0196	-
0.3266	46000	0.0002	-	-	-	-
0.3337	47000	0.0002	-	-	-	-
0.3408	48000	0.0002	-	-	-	-
0.3479	49000	0.0002	-	-	-	-
0.3550	50000	0.0002	0.0002	0.8665	-0.0192	-
0.3621	51000	0.0002	-	-	-	-
0.3692	52000	0.0002	-	-	-	-
0.3763	53000	0.0002	-	-	-	-
0.3834	54000	0.0002	-	-	-	-
0.3905	55000	0.0002	0.0002	0.8650	-0.0187	-
0.3976	56000	0.0002	-	-	-	-
0.4047	57000	0.0002	-	-	-	-
0.4118	58000	0.0002	-	-	-	-
0.4189	59000	0.0002	-	-	-	-
0.4260	60000	0.0002	0.0002	0.8636	-0.0184	-
0.4331	61000	0.0002	-	-	-	-
0.4402	62000	0.0002	-	-	-	-
0.4473	63000	0.0002	-	-	-	-
0.4544	64000	0.0002	-	-	-	-
0.4615	65000	0.0002	0.0002	0.8673	-0.0180	-
0.4686	66000	0.0002	-	-	-	-
0.4757	67000	0.0002	-	-	-	-
0.4828	68000	0.0002	-	-	-	-
0.4899	69000	0.0002	-	-	-	-
0.4970	70000	0.0002	0.0002	0.8692	-0.0178	-
0.5041	71000	0.0002	-	-	-	-
0.5112	72000	0.0002	-	-	-	-
0.5183	73000	0.0002	-	-	-	-
0.5254	74000	0.0002	-	-	-	-
0.5325	75000	0.0002	0.0002	0.8675	-0.0175	-
0.5396	76000	0.0002	-	-	-	-
0.5467	77000	0.0002	-	-	-	-
0.5538	78000	0.0002	-	-	-	-
0.5609	79000	0.0002	-	-	-	-
0.5680	80000	0.0002	0.0002	0.8657	-0.0173	-
0.5751	81000	0.0002	-	-	-	-
0.5822	82000	0.0002	-	-	-	-
0.5893	83000	0.0002	-	-	-	-
0.5964	84000	0.0002	-	-	-	-
0.6035	85000	0.0002	0.0002	0.8670	-0.0171	-
0.6106	86000	0.0002	-	-	-	-
0.6177	87000	0.0002	-	-	-	-
0.6248	88000	0.0002	-	-	-	-
0.6319	89000	0.0002	-	-	-	-
0.6390	90000	0.0002	0.0002	0.8665	-0.0169	-
0.6461	91000	0.0002	-	-	-	-
0.6532	92000	0.0002	-	-	-	-
0.6603	93000	0.0002	-	-	-	-
0.6674	94000	0.0002	-	-	-	-
0.6745	95000	0.0002	0.0002	0.8672	-0.0167	-
0.6816	96000	0.0002	-	-	-	-
0.6887	97000	0.0002	-	-	-	-
0.6958	98000	0.0002	-	-	-	-
0.7029	99000	0.0002	-	-	-	-
0.7100	100000	0.0002	0.0002	0.8657	-0.0165	-
0.7171	101000	0.0002	-	-	-	-
0.7242	102000	0.0002	-	-	-	-
0.7313	103000	0.0002	-	-	-	-
0.7384	104000	0.0002	-	-	-	-
0.7455	105000	0.0002	0.0002	0.8676	-0.0165	-
0.7526	106000	0.0002	-	-	-	-
0.7597	107000	0.0002	-	-	-	-
0.7668	108000	0.0002	-	-	-	-
0.7739	109000	0.0002	-	-	-	-
0.7810	110000	0.0002	0.0002	0.8672	-0.0164	-
0.7881	111000	0.0002	-	-	-	-
0.7952	112000	0.0002	-	-	-	-
0.8023	113000	0.0002	-	-	-	-
0.8094	114000	0.0002	-	-	-	-
0.8165	115000	0.0002	0.0002	0.8698	-0.0162	-
0.8236	116000	0.0002	-	-	-	-
0.8307	117000	0.0002	-	-	-	-
0.8378	118000	0.0002	-	-	-	-
0.8449	119000	0.0002	-	-	-	-
0.8520	120000	0.0002	0.0002	0.8685	-0.0161	-
0.8591	121000	0.0002	-	-	-	-
0.8662	122000	0.0002	-	-	-	-
0.8733	123000	0.0002	-	-	-	-
0.8804	124000	0.0002	-	-	-	-
0.8875	125000	0.0002	0.0002	0.8676	-0.0160	-
0.8946	126000	0.0002	-	-	-	-
0.9017	127000	0.0002	-	-	-	-
0.9088	128000	0.0002	-	-	-	-
0.9159	129000	0.0002	-	-	-	-
0.9230	130000	0.0002	0.0002	0.8682	-0.0159	-
0.9301	131000	0.0002	-	-	-	-
0.9372	132000	0.0002	-	-	-	-
0.9443	133000	0.0002	-	-	-	-
0.9514	134000	0.0002	-	-	-	-
0.9585	135000	0.0002	0.0002	0.8678	-0.0158	-
0.9656	136000	0.0002	-	-	-	-
0.9727	137000	0.0002	-	-	-	-
0.9798	138000	0.0002	-	-	-	-
0.9869	139000	0.0002	-	-	-	-
0.9940	140000	0.0002	0.0002	0.8685	-0.0158	-
-1	-1	-	-	-	-	0.8339

The bold row denotes the saved checkpoint.

Framework Versions

Python: 3.11.11
Sentence Transformers: 4.1.0
Transformers: 4.51.3
PyTorch: 2.7.1+cu118
Accelerate: 1.7.0
Datasets: 3.3.2
Tokenizers: 0.21.1

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",
}

MSELoss

bibtex

@inproceedings{reimers-2020-multilingual-sentence-bert,
    title = "Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation",
    author = "Reimers, Nils and Gurevych, Iryna",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing",
    month = "11",
    year = "2020",
    publisher = "Association for Computational Linguistics",
    url = "https://arxiv.org/abs/2004.09813",
}

	sentence	label
type	string	list
details	min: 4 tokens mean: 12.24 tokens max: 52 tokens	size: 768 elements

Mossie96/all-mpnet-base-v2_distilled_3_layers_1-5-10

SentenceTransformer based on sentence-transformers/all-mpnet-base-v2

Model Details

Model Description

Model Sources

Full Model Architecture

Usage

Direct Usage (Sentence Transformers)

Evaluation

Metrics

Semantic Similarity

Knowledge Distillation

Training Details

Training Dataset

Unnamed Dataset

Evaluation Dataset

Unnamed Dataset

Training Hyperparameters

Non-Default Hyperparameters

All Hyperparameters

Training Logs

Framework Versions

Citation

BibTeX

Sentence Transformers

MSELoss

Mossie96/
all-mpnet-base-v2_distilled_3_layers_1-5-10