This is a sentence-transformers model finetuned from sentence-transformers/all-mpnet-base-v2 on the json dataset. 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
Training Dataset:
- json

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.

python

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("maticzav/all-mpnet-legal")
# Run inference
sentences = [
    '1. The Board Member agrees that the Confidential Information he or she receives shall be used solely for the benefit of the HNBA Board Work, and that all rights to the proprietary and novel features contained in the Confidential Information are reserved by the disclosing Party. 6. The Board Member agrees that no license under any patent, copyright or other intellectual property right is granted by implication or otherwise to Board Members under this Agreement.',
    'Agreement shall not grant Receiving Party any right to Confidential Information.',
    'Receiving Party may share some Confidential Information with some third-parties (including consultants, agents and professional advisors).',
]
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,  0.0560,  0.5999],
#         [ 0.0560,  1.0000, -0.0040],
#         [ 0.5999, -0.0040,  1.0000]])

Evaluation

Metrics

Semantic Similarity

Dataset: contradiction-eval
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.8643
spearman_cosine	0.6321

Training Details

Training Dataset

json

Dataset: json
Size: 4,371 training samples
Columns: sentence1, sentence2, and score
Approximate statistics based on the first 1000 samples:
sentence1 sentence2 score
type string string int
details
min: 15 tokens
mean: 82.48 tokens
max: 384 tokens
min: 10 tokens
mean: 17.08 tokens
max: 30 tokens
0: ~78.80%
1: ~21.20%

Samples:

sentence1	sentence2	score
`5. All Confidential Information in any form and any medium, including all copies thereof, disclosed to the Recipient shall be returned to UNHCR or destroyed: (a) if a business relationship is not entered into with UNHCR on or before the date which is three (3) months after the date both Parties have signed the Agreement; or`	`Receiving Party shall destroy or return some Confidential Information upon the termination of Agreement.`	`0`
`4. Nothing in this Agreement is to be construed as granting the Recipient, by implication or otherwise, any right whatsoever with respect to the Confidential Information or part thereof.`	`Agreement shall not grant Receiving Party any right to Confidential Information.`	`0`
`11. The Recipient shall not advertise or otherwise make public the fact that it has a confidential relationship with UNHCR, nor shall the Recipient, in any manner whatsoever use the name, emblem, or official seal of the United Nations or UNHCR, or any abbreviation of the name of the United Nations or UNHCR in connection with its business or otherwise.`	`Receiving Party shall not disclose the fact that Agreement was agreed or negotiated.`	`0`

Loss: CosineSimilarityLoss with these parameters:
json
1{ 2 "loss_fct": "torch.nn.modules.loss.MSELoss" 3}

Training Hyperparameters

Non-Default Hyperparameters

per_device_train_batch_size: 16
learning_rate: 3.2154235098127886e-05
weight_decay: 0.08134688788363727
warmup_ratio: 0.17365303052048742
hub_model_id: maticzav/all-mpnet-legal

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: no
prediction_loss_only: True
per_device_train_batch_size: 16
per_device_eval_batch_size: 8
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: 3.2154235098127886e-05
weight_decay: 0.08134688788363727
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: 0.17365303052048742
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
bf16: False
fp16: False
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: False
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}
parallelism_config: None
deepspeed: None
label_smoothing_factor: 0.0
optim: adamw_torch_fused
optim_args: None
adafactor: False
group_by_length: False
length_column_name: length
project: huggingface
trackio_space_id: trackio
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: maticzav/all-mpnet-legal
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: no
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: True
prompts: None
batch_sampler: batch_sampler
multi_dataset_batch_sampler: proportional
router_mapping: {}
learning_rate_mapping: {}

Training Logs

Epoch	Step	Training Loss	contradiction-eval_spearman_cosine
0.1825	50	0.2247	-
0.3650	100	0.0832	-
0.5474	150	0.0647	-
0.7299	200	0.0592	-
0.9124	250	0.0523	-
1.0949	300	0.0516	-
1.2774	350	0.0394	-
1.4599	400	0.0275	-
1.6423	450	0.0337	-
1.8248	500	0.0351	-
2.0073	550	0.0251	-
2.1898	600	0.0218	-
2.3723	650	0.024	-
2.5547	700	0.0188	-
2.7372	750	0.0191	-
2.9197	800	0.0199	-
-1	-1	-	0.6321

Framework Versions

Python: 3.12.12
Sentence Transformers: 5.2.0
Transformers: 4.57.3
PyTorch: 2.9.0+cu126
Accelerate: 1.12.0
Datasets: 4.0.0
Tokenizers: 0.22.1

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",
}

	sentence1	sentence2	score
type	string	string	int
details	min: 15 tokens mean: 82.48 tokens max: 384 tokens	min: 10 tokens mean: 17.08 tokens max: 30 tokens	0: ~78.80% 1: ~21.20%