This is a sentence-transformers model finetuned from nreimers/albert-small-v2. 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: nreimers/albert-small-v2
Maximum Sequence Length: 512 tokens
Output Dimensionality: 768 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: AlbertModel 
  (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})
)

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 = [
    'What should the report on income tax information include as per Article 48c?',
    '7.\n\nMember States shall require subsidiary undertakings or branches not subject to the provisions of paragraphs 4 and 5 of this Article to publish and make accessible a report on income tax information where such subsidiary undertakings or branches serve no other objective than to circumvent the reporting requirements set out in this Chapter.\n\nArticle 48c\n\nContent of the report on income tax information\n\n1.\n\nThe report on income tax information required under Article 48b shall include information relating to all the activities of the standalone undertaking or ultimate parent undertaking, including those of all affiliated undertakings consolidated in the financial statements in respect of the relevant financial year.\n\n2.',
    '(d)\n\nseal any business premises and books or records for the period of time of, and to the extent necessary for, the inspection.\n\n3.\n\nThe undertaking or association of undertakings shall submit to inspections ordered by decision of the Commission. The officials and other accompanying persons authorised by the Commission to conduct an inspection shall exercise their powers upon production of a Commission decision:\n\n(a)\n\nspecifying the subject matter and purpose of the inspection;\n\n(b)\n\ncontaining a statement that, pursuant to Article 16, a lack of cooperation allows the Commission to take a decision on the basis of the facts that are available to it;\n\n(c)',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# 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	0.1148
cosine_accuracy@3	0.213
cosine_accuracy@5	0.2456
cosine_accuracy@10	0.289
cosine_precision@1	0.1148
cosine_precision@3	0.071
cosine_precision@5	0.0491
cosine_precision@10	0.0289
cosine_recall@1	0.1148
cosine_recall@3	0.213
cosine_recall@5	0.2456
cosine_recall@10	0.289
cosine_ndcg@10	0.1998
cosine_mrr@10	0.1715
cosine_map@100	0.1779

Training Details

Training Dataset

Unnamed Dataset

Size: 10 training samples
Columns: query_text and doc_text
Approximate statistics based on the first 10 samples:
query_text doc_text
type string string
details
min: 17 tokens
mean: 38.6 tokens
max: 89 tokens
min: 113 tokens
mean: 237.7 tokens
max: 512 tokens

Samples:

query_text	doc_text
`What are the requirements for Member States regarding the establishment of AI regulatory sandboxes, including the timeline for operational readiness and the possibility of joint establishment with other Member States?`	1. Member States shall ensure that their competent authorities establish at least one AI regulatory sandbox at national level, which shall be operational by 2 August 2026. That sandbox may also be established jointly with the competent authorities of other Member States. The Commission may provide technical support, advice and tools for the establishment and operation of AI regulatory sandboxes.[object Object][object Object]The obligation under the first subparagraph may also be fulfilled by participating in an existing sandbox in so far as that participation provides an equivalent level of national coverage for the participating Member States.
Member States must provide updates on their national energy and climate strategies, detailing the anticipated energy savings from 2021 to 2030. They are also obligated to report on the necessary energy savings and the policies intended to achieve these goals. If assessments reveal that a Member State's measures are inadequate to meet energy savings targets, the Commission may issue recommendations for improvement. Additionally, any shortfall in energy savings must be addressed in subsequent obligation periods.	9. Member States shall apply and calculate the effect of the options chosen under paragraph 8 for the period referred to in paragraph 1, first subparagraph, points (a) and (b)(i), separately:[object Object][object Object](a) for the calculation of the amount of energy savings required for the obligation period referred to in paragraph 1, first subparagraph, point (a), Member States may make use of the options listed in paragraph 8, points (a) to (d). All the options chosen under paragraph 8 taken together shall amount to no more than 25 % of the amount of energy savings referred to in paragraph 1, first subparagraph, point (a); (b) for the calculation of the amount of energy savings required for the obligation period referred to in paragraph 1, first subparagraph, point (b)(i), Member States may make use of the options listed in paragraph 8, points (b) to (g), provided that the individual actions referred to in paragraph 8, point (d), continue to have a verifiable and measurable impact after 31 December 2020. All...
`What is the functional definition of a remote biometric identification system, and how does it operate in terms of identifying individuals without their active participation?`	(17) The notion of ‘remote biometric identification system’ referred to in this Regulation should be defined functionally, as an AI system intended for the identification of natural persons without their active involvement, typically at a distance, through the comparison of a person’s biometric data with the biometric data contained in a reference database, irrespectively of the particular technology, processes or types of biometric data used. Such remote biometric identification systems are typically used to perceive multiple persons or their behaviour simultaneously in order to facilitate significantly the identification of natural persons without their active involvement. This excludes AI systems intended to be used for biometric

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
learning_rate: 2e-05
num_train_epochs: 1
warmup_ratio: 0.1
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: 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: 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: 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: 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: batch_sampler
multi_dataset_batch_sampler: proportional

Training Logs

Epoch	Step	cosine_ndcg@10
-1	-1	0.1998

Framework Versions

Python: 3.11.10
Sentence Transformers: 4.0.2
Transformers: 4.49.0
PyTorch: 2.6.0+cu124
Accelerate: 0.26.0
Datasets: 3.1.0
Tokenizers: 0.21.4

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}
}

	query_text	doc_text
type	string	string
details	min: 17 tokens mean: 38.6 tokens max: 89 tokens	min: 113 tokens mean: 237.7 tokens max: 512 tokens

amentaga-nttd/QA_FT_EMB_INC

SentenceTransformer based on nreimers/albert-small-v2

Model Details

Model Description

Model Sources

Full Model Architecture

Usage

Direct Usage (Sentence Transformers)

Evaluation

Metrics

Information Retrieval

Training Details

Training Dataset

Unnamed Dataset

Training Hyperparameters

Non-Default Hyperparameters

All Hyperparameters

Training Logs

Framework Versions

Citation

BibTeX

Sentence Transformers

MatryoshkaLoss

MultipleNegativesRankingLoss

amentaga-nttd/
QA_FT_EMB_INC