This is a sentence-transformers model finetuned from Alibaba-NLP/gte-multilingual-base on the csv dataset. 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: Alibaba-NLP/gte-multilingual-base
Maximum Sequence Length: 512 tokens
Output Dimensionality: 768 dimensions
Similarity Function: Cosine Similarity
Training Dataset:
- csv

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: NewModel 
  (1): Pooling({'word_embedding_dimension': 768, '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("sentence_transformers_model_id")
# Run inference
sentences = [
    'What is described in Item 8 of a financial reporting document?',
    'Item 8 refers to Financial Statements and Supplementary Data in the context of financial reporting.',
    'december 31,        | annual maturities ( in millions )\n------------------- | ---------------------------------\n2011                | $ 463                            \n2012                | 2014                             \n2013                | 2014                             \n2014                | 497                              \n2015                | 500                              \nthereafter          | 3152                             \ntotal recourse debt | $ 4612                           ',
]
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]

Training Details

Training Dataset

csv

Dataset: csv
Size: 8,585 training samples
Columns: query and corpus
Approximate statistics based on the first 1000 samples:
query corpus
type string string
details
min: 4 tokens
mean: 22.5 tokens
max: 81 tokens
min: 11 tokens
mean: 103.44 tokens
max: 512 tokens

Samples:

query	corpus
`What does the NVIDIA computing platform focus on accelerating?`	Data Center The NVIDIA computing platform is focused on accelerating the most compute-intensive workloads, such as AI, data analytics, graphics and scientific computing, across hyperscale, cloud, enterprise, public sector, and edge data centers. The platform consists of our energy efficient GPUs, data processing units, or DPUs, interconnects and systems, our CUDA programming model, and a growing body of software libraries, software development kits, or SDKs, application frameworks and services, which are either available as part of the platform or packaged and sold separately.
`What was the adjustment for Cadillac dealer strategy in 2023?`	`The adjustment for the Cadillac dealer strategy in 2023 was 175.`
`What is the standard of proof used in inter partes reviews (IPR) compared to federal district courts?`	`IPRs are conducted before Administrative Patent Judges in the USPTO using a lower standard of proof than used in federal district court and challenged patents are not accorded the presumption of validity.`

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

Evaluation Dataset

csv

Dataset: csv
Size: 905 evaluation samples
Columns: query and corpus
Approximate statistics based on the first 905 samples:
query corpus
type string string
details
min: 7 tokens
mean: 22.24 tokens
max: 83 tokens
min: 9 tokens
mean: 70.35 tokens
max: 512 tokens

Samples:

query	corpus
`What are the main ingredients used in the company's products?`	`The company uses a variety of ingredients in their products, including high fructose corn syrup, sucrose, aspartame, and other sweeteners, as well as ascorbic acid, citric acid, phosphoric acid, caffeine, and caramel color; they also use orange and other fruit juice concentrates, and water is a main ingredient in substantially all products.`
`What are the purposes of borrowings under the 2021 credit facility?`	`The 2021 credit facility is available for working capital, capital expenditures and other corporate purposes, including acquisitions and share repurchases.`
`What factors are considered in the revenue disaggregation process according to the guidance on segment reporting?`	We have considered (1) information that is regularly reviewed by our Chief Executive Officer, who has been identified as the chief operating decision maker (the 'CODM') as defined by the authoritative guidance on segment reporting, in evaluating financial performance and (2) disclosures presented outside of our financial statements in our earnings releases and used in investor presentations to disaggregate revenues. The principal category we use to disaggregate revenues is the nature of our products and subscriptions and services, as presented in our consolidated statements of operations.

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

Training Hyperparameters

Non-Default Hyperparameters

per_device_eval_batch_size: 16
learning_rate: 2e-05
num_train_epochs: 4
warmup_ratio: 0.1

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: no
prediction_loss_only: True
per_device_train_batch_size: 8
per_device_eval_batch_size: 16
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: 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: 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
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
0.4655	500	0.3183
0.9311	1000	0.1923
1.3966	1500	0.18
1.8622	2000	0.1697
2.3277	2500	0.1523
2.7933	3000	0.1494
3.2588	3500	0.1445
3.7244	4000	0.1334

Framework Versions

Python: 3.12.9
Sentence Transformers: 4.1.0
Transformers: 4.52.3
PyTorch: 2.7.0
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