This is a sentence-transformers model finetuned from am-azadi/gte-multilingual-base_Fine_Tuned_1e. 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: am-azadi/gte-multilingual-base_Fine_Tuned_1e
Maximum Sequence Length: 8192 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': 8192, '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 = [
    'Paul Pelosi’s DUI charges were dropped, by an order from Gavin Newsom. see how this works !?!',
    "DUI charges against Nancy Pelosi's husband dropped",
    'FRAUDE ELECTORAL Se están volviendo a contar las actas de varias mesas en Cantabria',
]
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

Unnamed Dataset

Size: 25,743 training samples
Columns: sentence_0, sentence_1, and label
Approximate statistics based on the first 1000 samples:
sentence_0 sentence_1 label
type string string float
details
min: 2 tokens
mean: 133.84 tokens
max: 5210 tokens
min: 5 tokens
mean: 20.52 tokens
max: 140 tokens
min: 1.0
mean: 1.0
max: 1.0

Samples:

sentence_0	sentence_1	label
`Assinando folhas em branco`	`Joe Biden assinou seus primeiros decretos como presidente dos Estados Unidos em folhas em branco`	`1.0`
`FIM DOS TEMPOS NOVA ZELÂNDIA PASSA A PERMITIR ABORTO ATÉ O NASCIMENTO. Parlamento ignora referendo popular e aprova lei. Texto nem exige que seja um médico a realizar o "procedimento". GIL DINIZ DEPUTADO ESTADUAL fto/carteiroreaca sensoCom a aprovação da lei, qualquer mulher poderá tirar a vida de seu bebê em qualquer fase da gravidez. Fim dos tempos!`	`Nova Zelândia passa a permitir aborto até o nascimento`	`1.0`
`बताईये... बाप बार डांसर उठा लाया था, बेटा पोर्न स्टार ही उठा लाया राहुल जी के कांग्रेसी! फिर कहते हैं EVM हैक हो गई... मल्लब हद है एकदम से भारत के विकास Love you Miya Happy Bujix 44 2.5`	`Indian National Congress workers feeding cake to a poster of a former porn actress Mia Khalifa`	`1.0`

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

Training Hyperparameters

Non-Default Hyperparameters

per_device_train_batch_size: 1
per_device_eval_batch_size: 1
num_train_epochs: 1
multi_dataset_batch_sampler: round_robin

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: no
prediction_loss_only: True
per_device_train_batch_size: 1
per_device_eval_batch_size: 1
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: 5e-05
weight_decay: 0.0
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
max_grad_norm: 1
num_train_epochs: 1
max_steps: -1
lr_scheduler_type: linear
lr_scheduler_kwargs: {}
warmup_ratio: 0.0
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
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: round_robin

Training Logs

Epoch	Step	Training Loss
0.0194	500	0.0
0.0388	1000	0.0
0.0583	1500	0.0
0.0777	2000	0.0
0.0971	2500	0.0
0.1165	3000	0.0
0.1360	3500	0.0
0.1554	4000	0.0
0.1748	4500	0.0
0.1942	5000	0.0
0.2137	5500	0.0
0.2331	6000	0.0
0.2525	6500	0.0
0.2719	7000	0.0
0.2913	7500	0.0
0.3108	8000	0.0
0.3302	8500	0.0
0.3496	9000	0.0
0.3690	9500	0.0
0.3885	10000	0.0
0.4079	10500	0.0
0.4273	11000	0.0
0.4467	11500	0.0
0.4661	12000	0.0
0.4856	12500	0.0
0.5050	13000	0.0
0.5244	13500	0.0
0.5438	14000	0.0
0.5633	14500	0.0
0.5827	15000	0.0
0.6021	15500	0.0
0.6215	16000	0.0
0.6410	16500	0.0
0.6604	17000	0.0
0.6798	17500	0.0
0.6992	18000	0.0
0.7186	18500	0.0
0.7381	19000	0.0
0.7575	19500	0.0
0.7769	20000	0.0
0.7963	20500	0.0
0.8158	21000	0.0
0.8352	21500	0.0
0.8546	22000	0.0
0.8740	22500	0.0
0.8934	23000	0.0
0.9129	23500	0.0
0.9323	24000	0.0
0.9517	24500	0.0
0.9711	25000	0.0
0.9906	25500	0.0

Framework Versions

Python: 3.11.11
Sentence Transformers: 3.4.1
Transformers: 4.48.3
PyTorch: 2.5.1+cu124
Accelerate: 1.3.0
Datasets: 3.3.2
Tokenizers: 0.21.0

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