modernbert-embed-base-biencoder-human-rights
This is a sentence-transformers model finetuned from nomic-ai/modernbert-embed-base. 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: nomic-ai/modernbert-embed-base
- Maximum Sequence Length: 8192 tokens
- Output Dimensionality: 768 dimensions
- Similarity Function: Cosine Similarity
- Language: en
- License: apache-2.0
Model Sources
Full Model Architecture
SentenceTransformer(
(0): Transformer({'max_seq_length': 8192, 'do_lower_case': False}) with Transformer model: ModernBertModel
(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
model = SentenceTransformer("sdiazlor/modernbert-embed-base-biencoder-human-rights")
sentences = [
"**US Civil Rights Act of 1964**\n\nThe landmark legislation outlawed segregation in public facilities, employment, and education. It protected individuals from discrimination based on race, color, religion, sex, and national origin. Title VII prohibits employment discrimination, Title II addressed public accommodations, and Title VI ensured equal access to education and federal funding.\n\n**Brown v. Board of Education (1954)**\n\nThe US Supreme Court decision declared segregation in public schools unconstitutional. The court ruled that separate educational facilities are inherently unequal, leading to the desegregation of schools across the US. This decision was a significant milestone in the Civil Rights Movement.\n\n**Canadian Charter of Rights and Freedoms**\n\nThe Canadian Charter, implemented in 1982, enshrines fundamental freedoms, including freedom of expression and equality before the law. Section 15 ensures equal protection and benefit of the law for all individuals, regardless of their identity.\n\n**Mandela's Fight against Apartheid**\n\nNelson Mandela played a pivotal role in the fight against apartheid in South Africa. His release from prison in 1990 marked a turning point in the struggle for equality and democracy. The African National Congress's efforts led to the establishment of a democratic government in 1994.\n\n**UN Declaration on Human Rights**\n\nThe Universal Declaration of Human Rights, adopted in 1948, outlines fundamental human rights and freedoms. Article 26 states that everyone has the right to education, while Article 7 emphasizes the prohibition of discrimination. These principles serve as a foundation for human rights globally.\n\n**Racial Discrimination Act 1975 (Australia)**\n\nThis Australian legislation makes it unlawful to discriminate against individuals based on their race, color, descent, or national or ethnic origin. The Act also prohibits indirect discrimination and promotes equal opportunity.\n\n**Civil Rights Act of 1967 (Canada)**\n\nThe Canadian Act prohibited discrimination in the provision of goods and services, accommodation, and employment. It was a significant step towards promoting equality and protecting the rights of marginalized groups in Canada.\n\n**Marbury v. Madison (1803)**\n\nIn this landmark US Supreme Court case, the court established the principle of judicial review. The decision ensured that the judiciary has the power to review and strike down laws that are deemed unconstitutional, safeguarding individual rights and liberties.\n\n**Equal Protection Clause**\n\nThe 14th Amendment to the US Constitution guarantees equal protection under the law for all citizens, regardless of their status. This clause has been instrumental in protecting the rights of marginalized groups and ensuring equal justice for all.\n\n**Women's Rights Movement**\n\nThe movement for women's suffrage and equality gained momentum in the late 19th and early 20th centuries. Key figures such as Elizabeth Cady Stanton and Susan B. Anthony led the charge for women's right to vote and equal rights in education and employment.\n\n**International Convention on the Elimination of All Forms of Racial Discrimination**\n\nAdopted in 1965, this international treaty obliges states to eliminate racial discrimination in all its forms. It promotes equality and encourages states to take proactive measures to prevent and combat racial discrimination.\n\n**The Unrepresented Nations and Peoples Organization (UNPO)**\n\nThis international organization advocates for the rights of unrepresented peoples and nations. The UNPO works towards promoting equality and self-determination for marginalized communities globally.\n\n**US Voting Rights Act of 1965**\n\nThis legislation protected the voting rights of African Americans and other minority groups. It eliminated literacy tests and ensured equal access to voting booths, contributing to increased voter turnout and representation.\n\n**Gideon v. Wainwright (1963)**\n\nIn this US Supreme Court case, the court ruled that indigent defendants have a right to an attorney in criminal cases. The decision ensured that individuals have access to equal justice, regardless of their financial situation.\n\n**Women's Right to Education**\n\nThe Convention on the Elimination of All Forms of Discrimination against Women (CEDAW) ensures equal access to education for women. The treaty promotes women's rights and encourages states to eliminate all forms of discrimination against women.",
'What is the significance of the landmark legislation that outlawed segregation in public facilities, employment, and education in the US?',
'What is the primary implication of the landmark legislation that outlawed racial segregation in public facilities, employment, and education across major international airlines and transportation systems in the US?',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
Evaluation
Metrics
Triplet
| Metric |
Value |
| cosine_accuracy |
0.9819 |
Training Details
Training Dataset
Unnamed Dataset
Evaluation Dataset
Unnamed Dataset
Training Hyperparameters
Non-Default Hyperparameters
eval_strategy: epoch
per_device_train_batch_size: 4
per_device_eval_batch_size: 4
gradient_accumulation_steps: 4
learning_rate: 2e-05
lr_scheduler_type: cosine
warmup_ratio: 0.1
use_mps_device: 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: epoch
prediction_loss_only: True
per_device_train_batch_size: 4
per_device_eval_batch_size: 4
per_gpu_train_batch_size: None
per_gpu_eval_batch_size: None
gradient_accumulation_steps: 4
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: cosine
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: True
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: 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
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: no_duplicates
multi_dataset_batch_sampler: proportional
Training Logs
| Epoch |
Step |
Training Loss |
Validation Loss |
cosine_accuracy |
| 1.0 |
42 |
- |
3.6559 |
0.9699 |
| 2.0 |
84 |
- |
3.5678 |
0.9880 |
| 2.3855 |
100 |
14.374 |
- |
- |
| 2.9398 |
123 |
- |
3.4984 |
0.9819 |
- The bold row denotes the saved checkpoint.
Framework Versions
- Python: 3.11.4
- Sentence Transformers: 3.3.1
- Transformers: 4.49.0.dev0
- PyTorch: 2.4.0
- Accelerate: 0.34.0
- Datasets: 2.21.0
- Tokenizers: 0.21.0
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",
}
TripletLoss
@misc{hermans2017defense,
title={In Defense of the Triplet Loss for Person Re-Identification},
author={Alexander Hermans and Lucas Beyer and Bastian Leibe},
year={2017},
eprint={1703.07737},
archivePrefix={arXiv},
primaryClass={cs.CV}
}