am-azadi/gte-multilingual-base_Fine_Tuned_2e

SentenceTransformer based on am-azadi/gte-multilingual-base_Fine_Tuned_1e

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.

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:

  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  

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

@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

@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}
}