This is a sentence-transformers model finetuned from Snowflake/snowflake-arctic-embed-l. It maps sentences & paragraphs to a 1024-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: Snowflake/snowflake-arctic-embed-l
Maximum Sequence Length: 512 tokens
Output Dimensionality: 1024 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': 512, 'do_lower_case': False}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, '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("llm-wizard/legal-ft")
# Run inference
sentences = [
    'What challenges do professional journalists and publishers face that may impact their ability to enforce their intellectual property rights?',
    '19 \nrespect. They feel very good about it. And in our user interface, even though we give the answer, \nwe do show the user exactly where the answer is coming from.”16  \n68. \nAs Srinivas surely knows or should know, academic standards for avoiding \nplagiarism are wholly independent from copyright law.17 Dow Jones and NYP Holdings editors \nand journalists are not graduate students working out of a library or lab, eager to have someone \nacknowledge and utilize their research. They are professional journalists and publishers – working \nunder high-pressure deadlines, sometimes in dangerous places – whose livelihoods depend on the \nenforcement and monetization of their intellectual property rights.  \n69.',
    'ban or prohibition on the use of AI by students. The Defendants were not trained on any policies \nor procedures for use of AI alone, never mind what they were “able to do” to students who used \nit.    The entire purpose behind having such policies and procedures in place is to ensure notice, \nequity, fairness and to be sure:  a level playing field for all.   Making matters worse, there exists \nno adequate procedures and policies for the induction of an applicant into NHS when compared to \nother members who are inducted despite the same or similar infractions.  This is a denial of student \nrights of the highest order. \n \nIn the case here, RNH was disciplined on an ad hoc and on-going basis over more than six',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

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

Evaluation

Metrics

Information Retrieval

Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.7292
cosine_accuracy@3	0.8542
cosine_accuracy@5	0.9375
cosine_accuracy@10	1.0
cosine_precision@1	0.7292
cosine_precision@3	0.2847
cosine_precision@5	0.1875
cosine_precision@10	0.1
cosine_recall@1	0.7292
cosine_recall@3	0.8542
cosine_recall@5	0.9375
cosine_recall@10	1.0
cosine_ndcg@10	0.8576
cosine_mrr@10	0.8125
cosine_map@100	0.8125

Training Details

Training Dataset

Unnamed Dataset

Size: 400 training samples
Columns: sentence_0 and sentence_1
Approximate statistics based on the first 400 samples:
sentence_0 sentence_1
type string string
details
min: 10 tokens
mean: 20.93 tokens
max: 35 tokens
min: 25 tokens
mean: 140.37 tokens
max: 260 tokens

Samples:

sentence_0	sentence_1
`What provisions of the 2023-2024 Handbook were referenced regarding the use of AI and academic integrity?`	13 [object Object] [object Object]procedure, expectation, conduct, discipline, sanction or consequence for the use of AI. Id. at ¶102. [object Object]Under these circumstances, the use of AI was not a violation of the then existing “Academic [object Object]Integrity: Cheating and Plagiarism” provisions of the 2023-2024 Handbook. Id. at ¶104. As such, [object Object]accusations of cheating, plagiarism, and academic misconduct or dishonesty were not supported [object Object]by the record evidence which, at all times relevant, the Defendants have had in their care, custody [object Object]and control. Id. at ¶105. [object Object]While there is much dispute as to whether the use of generative AI constitutes plagiarism, [object Object]plagiarism is defined as the practice of taking someone else’s work or ideas and passing them off
`How is plagiarism defined in the context provided?`	13 [object Object] [object Object]procedure, expectation, conduct, discipline, sanction or consequence for the use of AI. Id. at ¶102. [object Object]Under these circumstances, the use of AI was not a violation of the then existing “Academic [object Object]Integrity: Cheating and Plagiarism” provisions of the 2023-2024 Handbook. Id. at ¶104. As such, [object Object]accusations of cheating, plagiarism, and academic misconduct or dishonesty were not supported [object Object]by the record evidence which, at all times relevant, the Defendants have had in their care, custody [object Object]and control. Id. at ¶105. [object Object]While there is much dispute as to whether the use of generative AI constitutes plagiarism, [object Object]plagiarism is defined as the practice of taking someone else’s work or ideas and passing them off
`What is the case number associated with the document filed on 10/21/24?`	`program-ad-revenue-sharing-ai-time-fortune-der-spiegel. [object Object]Case 1:24-cv-07984 Document 1 Filed 10/21/24 Page 21 of 42`

Loss: MatryoshkaLoss with these parameters:
json
1{ 2 "loss": "MultipleNegativesRankingLoss", 3 "matryoshka_dims": [ 4 768, 5 512, 6 256, 7 128, 8 64 9 ], 10 "matryoshka_weights": [ 11 1, 12 1, 13 1, 14 1, 15 1 16 ], 17 "n_dims_per_step": -1 18}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 10
per_device_eval_batch_size: 10
num_train_epochs: 10
multi_dataset_batch_sampler: round_robin

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: steps
prediction_loss_only: True
per_device_train_batch_size: 10
per_device_eval_batch_size: 10
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: 5e-05
weight_decay: 0.0
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
max_grad_norm: 1
num_train_epochs: 10
max_steps: -1
lr_scheduler_type: linear
lr_scheduler_kwargs: {}
warmup_ratio: 0.0
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: 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}
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
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
use_liger_kernel: False
eval_use_gather_object: False
average_tokens_across_devices: False
prompts: None
batch_sampler: batch_sampler
multi_dataset_batch_sampler: round_robin

Training Logs

Epoch	Step	cosine_ndcg@10
1.0	40	0.8182
1.25	50	0.8172
2.0	80	0.8112
2.5	100	0.8414
3.0	120	0.8236
3.75	150	0.7962
4.0	160	0.7930
5.0	200	0.8536
6.0	240	0.8263
6.25	250	0.8257
7.0	280	0.8475
7.5	300	0.8505
8.0	320	0.8499
8.75	350	0.8582
9.0	360	0.8576
10.0	400	0.8576

Framework Versions

Python: 3.11.11
Sentence Transformers: 3.4.1
Transformers: 4.48.2
PyTorch: 2.5.1+cu124
Accelerate: 1.3.0
Datasets: 3.2.0
Tokenizers: 0.21.0

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",
}

MatryoshkaLoss

bibtex

@misc{kusupati2024matryoshka,
    title={Matryoshka Representation Learning},
    author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
    year={2024},
    eprint={2205.13147},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}

MultipleNegativesRankingLoss

bibtex

@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}
}

	sentence_0	sentence_1
type	string	string
details	min: 10 tokens mean: 20.93 tokens max: 35 tokens	min: 25 tokens mean: 140.37 tokens max: 260 tokens