metadata

base_model: sentence-transformers/paraphrase-MiniLM-L3-v2
datasets: []
language: []
library_name: sentence-transformers
metrics:
  - cosine_accuracy
  - dot_accuracy
  - manhattan_accuracy
  - euclidean_accuracy
  - max_accuracy
pipeline_tag: sentence-similarity
tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:2000
  - loss:MultipleNegativesRankingLoss
widget:
  - source_sentence: >-
      However, this Court will determine that there was sufficient evidence to
      sustain the jury's verdict if the evidence was "of such quality and weight
      that, having in mind the beyond a reasonable doubt burden of proof
      standard, reasonable fair-minded men in the exercise of impartial judgment
      might reach different conclusions on every element of the offense."
    sentences:
      - >-
        This Court will determine if there was enough evidence to support the
        jury's verdict by considering whether reasonable people could have
        reached different conclusions based on the evidence presented.
      - >-
        The VA psychiatrist believed that the Veteran was likely to have PTSD as
        a direct result of the attack on him during his military service in
        Korea.
      - >-
        The Veteran started seeing a mental health specialist at the VA on a
        regular basis.
  - source_sentence: >-
      Under such circumstances, VA is required to prove by clear and
      unmistakable evidence that a disease or injury manifesting in service both
      preexisted service and was not aggravated by service.
    sentences:
      - >-
        The independent mental health expert offered a comprehensive account of
        the Veteran's mental health issues, service-related impairments, and
        previous psychiatric and medical treatment experiences.
      - >-
        At the trial, the prosecution failed to provide a search warrant, which
        was not explained or justified.
      - >-
        In order to establish that a disease or injury did not arise from
        service, VA must provide clear and convincing evidence that the
        condition existed prior to military service and was not exacerbated by
        service.
  - source_sentence: >-
      Evidence of behavior changes following the claimed assault is one type of
      relevant evidence that may be found in these sources.
    sentences:
      - >-
        The independent medical clinician comprehensively documented the impact
        of the Veteran's alleged condition on their functional abilities.
      - >-
        A range of behavioral indicators, including alterations in demeanor,
        speech patterns, and physical reactions, can serve as valuable evidence
        in support of allegations of assault.
      - >-
        He claims that his mental health issues, which have been diagnosed as
        various psychiatric disorders, are a result of the trauma he experienced
        during his deployment to a combat zone in Vietnam while stationed in
        Japan in 1974.
  - source_sentence: >-
      The court held Apple had not made the requisite showing of likelihood of
      success on the merits because it “concluded that there is some doubt as to
      the copyrightability of the programs described in this litigation.”
    sentences:
      - >-
        The trial court committed a series of errors in this case, including
        failing to instruct the jury on an essential element of felonious damage
        to computers, denying the defendant's motion to dismiss, and entering
        judgment on a fatally flawed indictment.
      - >-
        The court determined that Apple had not provided sufficient evidence to
        demonstrate a likelihood of success on the merits, as it had "raised
        some doubts about the copyrightability of the programs in question."
      - >-
        The Veteran believes that she should be granted service connection for
        chronic PTSD or other psychiatric disorder because she has been
        diagnosed with chronic PTSD as a result of several stressful events that
        occurred during her periods of active duty and active duty for training
        with the Army National Guard.
  - source_sentence: >-
      In contrast, the scope of punishable conduct under the instant statute is
      limited by the individual's specified intent to "haras[s]" by
      communicating a "threat" so as to "engage in a knowing and willful course
      of conduct" directed at the victim that "alarms, torments, or terrorizes"
      the victim.
    sentences:
      - >-
        The scope of punishable conduct under the statute is limited to the
        individual's intent to harass by communicating a threat so as to engage
        in a knowing and willful course of conduct directed at the victim that
        alarms, torments, or terrorizes the victim.
      - >-
        The Veteran has been diagnosed with both major depressive disorder and
        PTSD.
      - >-
        The trial court's decision on an anti-SLAPP motion is subject to de novo
        review.
model-index:
  - name: SentenceTransformer based on sentence-transformers/paraphrase-MiniLM-L3-v2
    results:
      - task:
          type: triplet
          name: Triplet
        dataset:
          name: all nli dev
          type: all-nli-dev
        metrics:
          - type: cosine_accuracy
            value: 1
            name: Cosine Accuracy
          - type: dot_accuracy
            value: 0
            name: Dot Accuracy
          - type: manhattan_accuracy
            value: 1
            name: Manhattan Accuracy
          - type: euclidean_accuracy
            value: 1
            name: Euclidean Accuracy
          - type: max_accuracy
            value: 1
            name: Max Accuracy
      - task:
          type: triplet
          name: Triplet
        dataset:
          name: all nli test
          type: all-nli-test
        metrics:
          - type: cosine_accuracy
            value: 1
            name: Cosine Accuracy
          - type: dot_accuracy
            value: 0
            name: Dot Accuracy
          - type: manhattan_accuracy
            value: 1
            name: Manhattan Accuracy
          - type: euclidean_accuracy
            value: 1
            name: Euclidean Accuracy
          - type: max_accuracy
            value: 1
            name: Max Accuracy
          - type: cosine_accuracy
            value: 1
            name: Cosine Accuracy
          - type: dot_accuracy
            value: 0
            name: Dot Accuracy
          - type: manhattan_accuracy
            value: 1
            name: Manhattan Accuracy
          - type: euclidean_accuracy
            value: 1
            name: Euclidean Accuracy
          - type: max_accuracy
            value: 1
            name: Max Accuracy

SentenceTransformer based on sentence-transformers/paraphrase-MiniLM-L3-v2

This is a sentence-transformers model finetuned from sentence-transformers/paraphrase-MiniLM-L3-v2. It maps sentences & paragraphs to a 384-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/paraphrase-MiniLM-L3-v2
Maximum Sequence Length: 128 tokens
Output Dimensionality: 384 tokens
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': 128, 'do_lower_case': False}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 384, '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})
)

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("justArmenian/legal_paraphrase")
# Run inference
sentences = [
    'In contrast, the scope of punishable conduct under the instant statute is limited by the individual\'s specified intent to "haras[s]" by communicating a "threat" so as to "engage in a knowing and willful course of conduct" directed at the victim that "alarms, torments, or terrorizes" the victim.',
    "The scope of punishable conduct under the statute is limited to the individual's intent to harass by communicating a threat so as to engage in a knowing and willful course of conduct directed at the victim that alarms, torments, or terrorizes the victim.",
    'The Veteran has been diagnosed with both major depressive disorder and PTSD.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

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

Evaluation

Metrics

Triplet

Dataset: all-nli-dev
Evaluated with TripletEvaluator

Metric	Value
cosine_accuracy	1.0
dot_accuracy	0.0
manhattan_accuracy	1.0
euclidean_accuracy	1.0
max_accuracy	1.0

Triplet

Dataset: all-nli-test
Evaluated with TripletEvaluator

Metric	Value
cosine_accuracy	1.0
dot_accuracy	0.0
manhattan_accuracy	1.0
euclidean_accuracy	1.0
max_accuracy	1.0

Triplet

Dataset: all-nli-test
Evaluated with TripletEvaluator

Metric	Value
cosine_accuracy	1.0
dot_accuracy	0.0
manhattan_accuracy	1.0
euclidean_accuracy	1.0
max_accuracy	1.0

Training Details

Training Dataset

Unnamed Dataset

Size: 2,000 training samples
Columns: anchor, positive, and negative

Approximate statistics based on the first 1000 samples:

	anchor	positive	negative
type	string	string	string
details	min: 8 tokens mean: 36.01 tokens max: 128 tokens	min: 8 tokens mean: 31.41 tokens max: 99 tokens	min: 8 tokens mean: 31.39 tokens max: 99 tokens

Samples:

anchor	positive	negative
`The weight of the competent and probative medical opinions of record is against finding that the Veteran has a current diagnosis of PTSD or any other chronic acquired psychiatric disorder which is related to her military service.`	`The weight of the credible and persuasive medical evidence on record suggests that the Veteran does not currently suffer from PTSD or any other chronic psychiatric condition related to her military service.`	`It is evident that an unauthorized physical intrusion would have been deemed a "search" under the Fourth Amendment when it was originally formulated.`
`We have no doubt that such a physical intrusion would have been considered a “search” within the meaning of the Fourth Amendment when it was adopted.`	`It is evident that an unauthorized physical intrusion would have been deemed a "search" under the Fourth Amendment when it was originally formulated.`	`In June 1972, the Veteran's condition was assessed by the Army Medical Board, which concluded that the Veteran's back condition made him unfit for active service, leading to his discharge from the military.`
`Later in June 1972, the Veteran's condition was evaluated by the Army Medical Board, where it was determined that the Veteran's back condition rendered him physically unfit for active service, and he was subsequently discharged from service.`	`In June 1972, the Veteran's condition was assessed by the Army Medical Board, which concluded that the Veteran's back condition made him unfit for active service, leading to his discharge from the military.`	`The court has granted a petition for a writ of certiorari to review a decision made by the Court of Appeal of California, Fourth Appellate District, Division One.`

Loss: MultipleNegativesRankingLoss with these parameters:

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

Evaluation Dataset

Unnamed Dataset

Size: 500 evaluation samples
Columns: anchor, positive, and negative

Approximate statistics based on the first 1000 samples:

	anchor	positive	negative
type	string	string	string
details	min: 8 tokens mean: 35.69 tokens max: 128 tokens	min: 8 tokens mean: 32.11 tokens max: 77 tokens	min: 8 tokens mean: 32.12 tokens max: 77 tokens

Samples:

anchor	positive	negative
`(Virginia v. Black, supra, 538 U.S. at p. 347.)`	`The Black Court asserted that the "vagueness doctrine is a safeguard against the arbitrary exercise of power by government officials."`	`This Court will determine if there was enough evidence to support the jury's verdict by considering whether reasonable people could have reached different conclusions based on the evidence presented.`
`However, this Court will determine that there was sufficient evidence to sustain the jury's verdict if the evidence was "of such quality and weight that, having in mind the beyond a reasonable doubt burden of proof standard, reasonable fair-minded men in the exercise of impartial judgment might reach different conclusions on every element of the offense."`	`This Court will determine if there was enough evidence to support the jury's verdict by considering whether reasonable people could have reached different conclusions based on the evidence presented.`	`The VA psychiatrist believed that the Veteran was likely to have PTSD as a direct result of the attack on him during his military service in Korea.`
`This VA psychiatrist opined that the Veteran had PTSD more likely than not to be the direct result of the attack on him during service in Korea.`	`The VA psychiatrist believed that the Veteran was likely to have PTSD as a direct result of the attack on him during his military service in Korea.`	`She noted that the Veteran's greatest source of stress was caring for their adult child without any assistance.`

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: 16
per_device_eval_batch_size: 16
num_train_epochs: 1
warmup_ratio: 0.1
fp16: 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: 16
per_device_eval_batch_size: 16
per_gpu_train_batch_size: None
per_gpu_eval_batch_size: None
gradient_accumulation_steps: 1
eval_accumulation_steps: None
learning_rate: 5e-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: 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: 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}
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: False
hub_always_push: False
gradient_checkpointing: False
gradient_checkpointing_kwargs: None
include_inputs_for_metrics: False
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
dispatch_batches: None
split_batches: 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
batch_sampler: no_duplicates
multi_dataset_batch_sampler: proportional

Training Logs

Epoch	Step	Training Loss	loss	all-nli-dev_max_accuracy	all-nli-test_max_accuracy
0	0	-	-	1.0	-
0.08	10	0.1402	0.0759	1.0	-
0.16	20	0.0873	0.0726	1.0	-
0.24	30	0.0992	0.0677	1.0	-
0.32	40	0.0759	0.0651	1.0	-
0.4	50	0.0355	0.0652	1.0	-
0.48	60	0.0814	0.0666	1.0	-
0.56	70	0.0353	0.0677	1.0	-
0.64	80	0.1404	0.0677	1.0	-
0.72	90	0.0336	0.0664	1.0	-
0.8	100	0.0559	0.0661	1.0	-
0.88	110	0.0484	0.0654	1.0	-
0.96	120	0.0522	0.0650	1.0	-
1.0	125	-	-	-	1.0

Framework Versions

Python: 3.10.12
Sentence Transformers: 3.0.1
Transformers: 4.42.4
PyTorch: 2.3.1+cu121
Accelerate: 0.32.1
Datasets: 2.20.0
Tokenizers: 0.19.1

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}
}