This is a sentence-transformers model finetuned from intfloat/multilingual-e5-large-instruct on the mcqa-rag-finetune 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: intfloat/multilingual-e5-large-instruct
Maximum Sequence Length: 512 tokens
Output Dimensionality: 1024 dimensions
Similarity Function: Cosine Similarity
Training Dataset:
- mcqa-rag-finetune

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: XLMRobertaModel 
  (1): Pooling({'word_embedding_dimension': 1024, '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("sentence_transformers_model_id")
# Run inference
sentences = [
    'Hipotalamusi NUK kontrollon sekretimin e hormoneve:\nA. FSH dhe LH\nB. te rritjes(GH)\nC. ACTH\nD. te pankreasit',
    'Hipotalamusi është një pjesë e trurit që ndodhet nën talamusin. Ai luan një rol kryesor në lidhjen e sistemit nervor me sistemin endokrin përmes gjëndrës së hipofizës.',
    'State laws that regulate matters of legitimate local concern but have an incidental effect on interstate commerce are subject to a less strict balancing test. Under this test, a state law will be upheld unless the burden imposed on interstate commerce is clearly excessive in relation to the putative local benefits.',
]
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]

Training Details

Training Dataset

mcqa-rag-finetune

Dataset: mcqa-rag-finetune at d1f5446
Size: 594,028 training samples
Columns: anchor and positive
Approximate statistics based on the first 1000 samples:
anchor positive
type string string
details
min: 22 tokens
mean: 105.96 tokens
max: 512 tokens
min: 12 tokens
mean: 70.95 tokens
max: 478 tokens

Samples:

anchor	positive
`Find all c in Z_3 such that Z_3[x]/(x^2 + c) is a field.[object Object]A. 0[object Object]B. 1[object Object]C. 2[object Object]D. 3`	`The notation Z_3 refers to the finite field with three elements, often denoted as {0, 1, 2}. This field operates under modular arithmetic, specifically modulo 3. Elements in Z_3 can be added and multiplied according to the rules of modulo 3, where any number can wrap around upon reaching 3.`
`Find all c in Z_3 such that Z_3[x]/(x^2 + c) is a field.[object Object]A. 0[object Object]B. 1[object Object]C. 2[object Object]D. 3`	`A field is a set equipped with two operations, addition and multiplication, satisfying certain properties: associativity, commutativity, distributivity, the existence of additive and multiplicative identities, and the existence of additive inverses and multiplicative inverses (for all elements except the zero element). In order for Z_3[x]/(f(x)) to be a field, the polynomial f(x) must be irreducible over Z_3.`
`Find all c in Z_3 such that Z_3[x]/(x^2 + c) is a field.[object Object]A. 0[object Object]B. 1[object Object]C. 2[object Object]D. 3`	`The expression Z_3[x] indicates the set of all polynomials with coefficients in Z_3. A polynomial is said to be irreducible over Z_3 if it cannot be factored into the product of two non-constant polynomials with coefficients in Z_3. In the case of quadratic polynomials like x^2 + c, irreducibility depends on whether it has any roots in the field Z_3.`

Loss: MultipleNegativesRankingLoss with these parameters:
json
1{ 2 "scale": 20.0, 3 "similarity_fct": "cos_sim" 4}

Evaluation Dataset

mcqa-rag-finetune

Dataset: mcqa-rag-finetune at d1f5446
Size: 1,000 evaluation samples
Columns: anchor and positive
Approximate statistics based on the first 1000 samples:
anchor positive
type string string
details
min: 22 tokens
mean: 98.74 tokens
max: 512 tokens
min: 9 tokens
mean: 59.88 tokens
max: 501 tokens

Samples:

anchor	positive
`ക്രൂരകോഷ്ഠം ഉള്ള ഒരാളിൽ കോപിച്ചിരിക്കുന്ന ദോഷം താഴെപ്പറയുന്നവയിൽ ഏതാണ്?[object Object]A. കഫം[object Object]B. പിത്തം[object Object]C. വാതം[object Object]D. രക്തം`	`ഓരോ ദോഷത്തിനും അതിന്റേതായ സ്വഭാവങ്ങളും ശരീരത്തിൽ അത് ഉണ്ടാക്കുന്ന ഫലങ്ങളും ഉണ്ട്.`
`Melyik tényező nem befolyásolja a fagylalt keresleti függvényét?[object Object]A. A fagylalt árának változása.[object Object]B. Mindegyik tényező befolyásolja.[object Object]C. A jégkrém árának változása.[object Object]D. A fagylalttölcsér árának változása.`	`A keresleti függvény negatív meredekségű, ami azt jelenti, hogy az ár növekedésével a keresett mennyiség csökken (csökkenő kereslet törvénye).`
In contrast to _______, _______ aim to reward favourable behaviour by companies. The success of such campaigns have been heightened through the use of ___________, which allow campaigns to facilitate the company in achieving _________ .[object Object]A. Boycotts, Buyalls, Blockchain technology, Increased Sales[object Object]B. Buycotts, Boycotts, Digital technology, Decreased Sales[object Object]C. Boycotts, Buycotts, Digital technology, Decreased Sales[object Object]D. Buycotts, Boycotts, Blockchain technology, Charitable donations[object Object]E. Boycotts, Buyalls, Blockchain technology, Charitable donations[object Object]F. Boycotts, Buycotts, Digital technology, Increased Sales[object Object]G. Buycotts, Boycotts, Digital technology, Increased Sales[object Object]H. Boycotts, Buycotts, Physical technology, Increased Sales[object Object]I. Buycotts, Buyalls, Blockchain technology, Charitable donations[object Object]J. Boycotts, Buycotts, Blockchain technology, Decreased Sales	`[object Object]: This term refers to the actions taken by consumers to promote social, political, or environmental causes. These actions can include boycotting certain companies or buycotting others, influencing market dynamics based on ethical considerations. The effectiveness of consumer activism can vary but has gained prominence in recent years with increased visibility through social media.`

Loss: MultipleNegativesRankingLoss with these parameters:
json
1{ 2 "scale": 20.0, 3 "similarity_fct": "cos_sim" 4}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 12
per_device_eval_batch_size: 12
learning_rate: 3e-05
num_train_epochs: 1
warmup_steps: 5000
fp16: True
load_best_model_at_end: True

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: steps
prediction_loss_only: True
per_device_train_batch_size: 12
per_device_eval_batch_size: 12
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: 3e-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: 0.0
warmup_steps: 5000
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: 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
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: proportional

Training Logs

Epoch	Step	Training Loss	Validation Loss
0.05	2476	0.1209	0.0347
0.1000	4952	0.0737	0.0459
0.1501	7428	0.087	0.0732
0.2001	9904	0.0825	0.1209
0.2501	12380	0.0783	0.0934
0.3001	14856	0.071	0.0793
0.3501	17332	0.0661	0.0855
0.4001	19808	0.0652	0.0964
0.4502	22284	0.063	0.0892
0.5002	24760	0.056	0.0923
0.5502	27236	0.0509	0.1016
0.6002	29712	0.045	0.0918
0.6502	32188	0.0472	0.0896
0.7002	34664	0.0396	0.0959
0.7503	37140	0.0371	0.0819
0.8003	39616	0.0341	0.0845
0.8503	42092	0.0344	0.0790
0.9003	44568	0.0288	0.0863
0.9503	47044	0.03	0.0767

The bold row denotes the saved checkpoint.

Framework Versions

Python: 3.11.9
Sentence Transformers: 4.1.0
Transformers: 4.52.3
PyTorch: 2.7.0+cu126
Accelerate: 1.7.0
Datasets: 3.6.0
Tokenizers: 0.21.1

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",
}

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}
}

	anchor	positive
type	string	string
details	min: 22 tokens mean: 105.96 tokens max: 512 tokens	min: 12 tokens mean: 70.95 tokens max: 478 tokens

DoDucAnh/MNLP_M2_document_encoder

SentenceTransformer based on intfloat/multilingual-e5-large-instruct

Model Details

Model Description

Model Sources

Full Model Architecture

Usage

Direct Usage (Sentence Transformers)

Training Details

Training Dataset

mcqa-rag-finetune

Evaluation Dataset

mcqa-rag-finetune

Training Hyperparameters

Non-Default Hyperparameters

All Hyperparameters

Training Logs

Framework Versions

Citation

BibTeX

Sentence Transformers

MultipleNegativesRankingLoss

DoDucAnh/
MNLP_M2_document_encoder