BGE m3 Uzbek Legal Matryoshka

This is a sentence-transformers model finetuned from BAAI/bge-m3 on the json dataset. 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: BAAI/bge-m3
Maximum Sequence Length: 8192 tokens
Output Dimensionality: 1024 dimensions
Similarity Function: Cosine Similarity
Training Dataset:
- json
Language: uz
License: apache-2.0

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': 8192, 'do_lower_case': False}) with Transformer model: XLMRobertaModel 
  (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.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("fitlemon/bge-m3-uz-legal-matryoshka")
# Run inference
sentences = [
    'O‘n olti yoshga to‘lguniga qadar nogironligi bo‘lgan bolani tarbiyalayotgan ota-onaga qanday qo‘shimcha kunlar beriladi, {chapter} va {section}da bu haqida nima yozilgan?',
    'Xodimga dam olish uchun emas, balki boshqa maqsadlarda beriladigan, xodimni mehnat \nmajburiyatlarini bajarishdan ozod etish davrlari dam olish vaqtiga kirmaydi. Bunday davrlar \njumlasiga quyidagilar kiradi:  \nmehnat shartnomasi ish beruvchining tashabbusiga k o‘ra bekor qilinishi to‘g‘risidagi \nogohlantirish muddati davrida xodimga ishga joylashish uchun beriladigan ishdan bo‘sh bo‘linadigan \nqo‘shimcha kunlar; \no‘n olti yoshga to‘lguniga qadar nogironligi bo‘lgan bolani tarbiyalayotgan ota -onadan \nbiriga (ota -onaning o‘rnini bosuvchi shaxsga) beriladigan ishdan bo‘sh bo‘linadigan qo‘shimcha \nkunlar; \nhomilador ayollarga beriladigan ishdan bo‘sh bo‘linadigan kunlar; \ndonorlarning tibbiy tekshiruv kunida hamda qon va uning tarkibiy qismlari topshiriladigan \nkunda ishdan ozod etilishi;  \nijtimoiy ta’tillar: homiladorlik va tug‘ish ta’tillari, bolani parvarishlash ta’tillari, o‘quv \nta’tillari va ijodiy ta’tillar; \nxodim tomonidan davlat yoki jamoat majburiyatlari bajariladigan davrlar; \nish beruvchining va mehnat jamoasining m anfaatlarini ko‘zlab majburiyatlar bajariladigan \ndavrlar; \nxodimning vaqtincha mehnatga qobiliyatsizlik davrlari; \nxodimga dam olish uchun emas, balki mehnat to‘g‘risidagi qonunchilikda va mehnat \nhaqidagi boshqa huquqiy hujjatlarda belgilangan o‘zga maqsadla rda beriladigan, xodimni mehnat \nmajburiyatlarini bajarishdan ozod etishning boshqa davrlari.',
    'Masofadan turib ishlovchi xodim bilan tuzilgan mehnat shartnomasi ushbu Kodeksda \nbelgilangan asoslarga ko‘ra bekor qilinishi mumkin. \nAgar masofadan turib ishlovchi xodimning ish beruvchining masofadan turib ishlash \nto‘g‘risidagi mehnat shartnomasini bekor qilish to‘g‘risidagi buyrug‘i bilan tanishib chiqishi elektron \nhujjat tarzida amalga oshirilsa, ish beruvchi masofadan turib ishlovchi xodimga mazkur mehnat \nshartnomasi bekor qilingan kuni lozim darajada rasmiylashtirilgan mehnat shartnomasini bekor qilish \nto‘g‘risidagi buyruqning ko‘chirma nusxasini ma’lum qilinadigan buyurtma xat bilan pochta orqali \nqog‘ozda yuborishi shart. \n4-§. Vaxta usulida ishlovchi shaxslarning mehnatini huquqiy jihatdan tartibga solishning \no‘ziga xos xususiyatlari',
]
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

Datasets: dim_1024, dim_768, dim_512, dim_256, dim_128 and dim_64
Evaluated with InformationRetrievalEvaluator

Metric	dim_1024	dim_768	dim_512	dim_256	dim_128	dim_64
cosine_accuracy@1	0.6471	0.6433	0.6414	0.6205	0.6224	0.5939
cosine_accuracy@3	0.8349	0.8368	0.833	0.8216	0.8178	0.7951
cosine_accuracy@5	0.8918	0.8956	0.8937	0.8843	0.8672	0.8577
cosine_accuracy@10	0.9355	0.9336	0.9317	0.926	0.9184	0.9146
cosine_precision@1	0.6471	0.6433	0.6414	0.6205	0.6224	0.5939
cosine_precision@3	0.2783	0.2789	0.2777	0.2739	0.2726	0.265
cosine_precision@5	0.1784	0.1791	0.1787	0.1769	0.1734	0.1715
cosine_precision@10	0.0935	0.0934	0.0932	0.0926	0.0918	0.0915
cosine_recall@1	0.6471	0.6433	0.6414	0.6205	0.6224	0.5939
cosine_recall@3	0.8349	0.8368	0.833	0.8216	0.8178	0.7951
cosine_recall@5	0.8918	0.8956	0.8937	0.8843	0.8672	0.8577
cosine_recall@10	0.9355	0.9336	0.9317	0.926	0.9184	0.9146
cosine_ndcg@10	0.7946	0.7933	0.7915	0.7779	0.7727	0.7555
cosine_mrr@10	0.7489	0.7476	0.7458	0.7297	0.7257	0.7043
cosine_map@100	0.7528	0.7515	0.7497	0.7338	0.7303	0.7088

Training Details

Training Dataset

json

Dataset: json
Size: 4,737 training samples
Columns: question and chunk
Approximate statistics based on the first 1000 samples:
question chunk
type string string
details
min: 9 tokens
mean: 22.45 tokens
max: 53 tokens

min: 26 tokens
mean: 268.97 tokens
max: 520 tokens

	question	chunk
type	string	string
details	min: 9 tokens mean: 22.45 tokens max: 53 tokens	min: 26 tokens mean: 268.97 tokens max: 520 tokens

Samples:

question	chunk
`Ish beruvchi o‘rindoshlik asosida ishga qabul qilishda qanday hujjatlarni talab qilishga haqli emas?`	Boshqa ish beruvchiga (asosiy ish joyidan tashqari) o‘rindoshlik asosida ishga kirayotgan shaxslar quyidagilarni taqdim etishi shart: pasportni yoxud uning o‘rnini bosuvchi hujjatni yoki identifikatsiya ID-kartasini; asosiy ish joyidan O ‘zbekiston Respublikasi Bandlik va mehnat munosabatlari vazirligi tomonidan tasdiqlanadigan shakl bo‘yicha ma’lumotnomani; bajarilishi uchun qonunchilikka muvofiq faqat muayyan ish stajiga ega bo‘lgan shaxslar qo‘yilishi mumkin bo‘lgan ishga o‘rindoshlik a sosida qabul qilishda asosiy ish joyidagi mehnat daftarchasining tasdiqlangan ko‘chirma nusxasini yoki elektron mehnat daftarchasidan ko‘chirmani; diplomni, guvohnomani (sertifikatni) yoki ta’lim to‘g‘risidagi yoki kasbiy tayyorgarlik haqidagi boshqa hujjatni, agar bunday ish maxsus bilimlarni yoxud maxsus tayyorgarlikni talab qilsa; mehnat sharoitlari zararli va (yoki) xavfli bo‘lgan ishga qabul qilish chog‘ida asosiy ish joyidan mehnatning xususiyati va shartlari to‘g‘risidagi olingan m...
`Yakka tartibdagi mehnatga oid munosabatlarni tartibga solishning asosiy jihatlari nimalardan iborat?`	Yakka tartibdagi mehnatga oid munosabatlarni va ular bilan bevosita bog‘liq bo‘lgan ijtimoiy munosabatlarni huquqiy jihatdan tartibga solishning asosiy prinsiplari quyidagilardan iborat: mehnat huquqlarining tengligi, mehnat va mashg‘ulotlar sohasida kamsitishni taqiqlash; mehnat erkinligi va majburiy mehnatni taqiqlash; mehnat sohasidagi ijtimoiy sheriklik; mehnat huquqlari ta’minlanishining va mehnat majburiyatlari bajarilishining kafolatlanganligi; xodimning huquqiy holati yomonlashishiga yo‘l qo‘yilmasligi.
`Tashkilotning ta’sis hujjatlari ish beruvchining huquqlarini qanday ta'sir qiladi?`	`Ish beruvchi moddiy zarar yetkazilgan aniq sharoitlarni hisobga olgan holda zararni aybdor xodimdan to‘liq yoki qisman undirishdan voz kechish huquq iga ega. Tashkilot mulkdori ish beruvchining mazkur huquqini qonunchilikda, shuningdek tashkilotning ta’sis hujjatlarida nazarda tutilgan hollarda cheklashi mumkin.`

Loss: MatryoshkaLoss with these parameters:

{
    "loss": "MultipleNegativesRankingLoss",
    "matryoshka_dims": [
        1024,
        768,
        512,
        256,
        128,
        64
    ],
    "matryoshka_weights": [
        1,
        1,
        1,
        1,
        1,
        1
    ],
    "n_dims_per_step": -1
}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: epoch
learning_rate: 2e-05
num_train_epochs: 4
lr_scheduler_type: cosine
warmup_ratio: 0.1
fp16: True
tf32: False
load_best_model_at_end: True
optim: adamw_torch_fused
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: 8
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: 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: 4
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: 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: False
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_fused
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