This is a sentence-transformers model finetuned from am-azadi/UAE-Large-V1_Fine_Tuned. 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: am-azadi/UAE-Large-V1_Fine_Tuned
Maximum Sequence Length: 512 tokens
Output Dimensionality: 1024 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: BertModel 
  (1): Pooling({'word_embedding_dimension': 1024, '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})
)

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 = [
    'Look what a show Pope Francis gave in yesterday\'s homily / sermon! It\'s to be read and reread over and over again... This is the most spiritual Pope since Peter. "You may have flaws, be anxious, and sometimes live irritated, but don\'t forget that your life is the greatest company in the world. Only you can prevent it from going into decline. Many appreciate you, admire you and love you. remember that being happy is not having a sky without storms, a road without accidents, work without fatigue, relationships without disappointments. Being happy is finding strength in forgiveness, hope in battles, security in the stage of fear, love in discord. It\'s not just appreciating the smile, but also reflecting on sadness. It\'s not just celebrating successes, but learning lessons from failures. It\'s not just feeling happy with applause, but being happy in anonymity. Being happy is recognizing that it\'s worth life is worth living, despite all the challenges, misunderstandings, periods of crisis. Being happy is not a fatality of fate, but an achievement for those who manage to travel within themselves. To be happy is to stop feeling like a victim of problems and become the author of his own story . It\'s crossing deserts outside of yourself, but managing to find an oasis in the depths of our soul. It is to thank God for each morning, for the miracle of life. Being happy is not being afraid of your own feelings. It\'s knowing how to talk about yourself. It\'s having the courage to hear a "no". It\'s feeling safe when receiving criticism, even if unfair. It\'s kissing the children, pampering the parents, living poetic moments with friends, even when they hurt us. To be happy is to let the creature that lives in each of us live, free, joyful and simple. It\'s having maturity to be able to say: "I was wrong". It\'s having the courage to say, "I\'m sorry". It\'s having the sensitivity to say: "I need you". It\'s having the ability to say, "I love you". May your life become a garden of opportunities to be happy... May your springtime be a lover of joy. May in your winters be a lover of wisdom. And that when you make a mistake, start over from the beginning. For only then will you be in love with life. You will discover that being happy is not having a perfect life. But using tears to irrigate tolerance. Use losses to train patience. Using mistakes to sculpt serenity. Using pain to cut pleasure. Use obstacles to open intelligence windows. Never give up....Never give up on the people who love you. Never give up happiness, for life is an incredible spectacle." (Pope Francis).',
    '"The message that Pope Francis gave in yesterday\'s homily/sermon! It is to be read and reread several times... What an admirable man!"',
    'Denmark allows Muslim women to wear the niqab',
]
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

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: 6 tokens
mean: 113.55 tokens
max: 512 tokens
min: 4 tokens
mean: 17.89 tokens
max: 126 tokens
min: 1.0
mean: 1.0
max: 1.0

Samples:

sentence_0	sentence_1	label
`de. has left the route with his the ultimate nazi prey dogs. With his patches with swastikas of Tito Adolfito Hitler and those little things. But don't tell them fucking Nazis, they'll be offended later 180 * Rov`	`Santiago Abascal posed next to a man wearing Nazi emblems at a biker rally in Valladolid`	`1.0`
`the info is ... a Danish police officer told a woman who was wearing a veil that the parliament had decided to approve the use of the niqab for Muslim women in Denmark. The situation is reversed in Indonesia, which (he said) has a Muslim majority population. Aya naon with the country ieu?`	`Denmark allows Muslim women to wear the niqab`	`1.0`
`In Kolwezi, a Congolese driver destroys Chinese trucks out of anger`	`A Congolese destroys the trucks of a Chinese company in the DRC`	`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: 2
per_device_eval_batch_size: 2
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: 2
per_device_eval_batch_size: 2
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
0.0388	500	0.0173
0.0777	1000	0.0124
0.1165	1500	0.0127
0.1554	2000	0.0256
0.1942	2500	0.0123
0.2331	3000	0.0199
0.2719	3500	0.0079
0.3108	4000	0.0134
0.3496	4500	0.0127
0.3884	5000	0.026
0.4273	5500	0.0314
0.4661	6000	0.0267
0.5050	6500	0.0145
0.5438	7000	0.0093
0.5827	7500	0.007
0.6215	8000	0.0071
0.6603	8500	0.0116
0.6992	9000	0.0085
0.7380	9500	0.0157
0.7769	10000	0.0051
0.8157	10500	0.0101
0.8546	11000	0.0174
0.8934	11500	0.0116
0.9323	12000	0.0073
0.9711	12500	0.0146

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: 3.3.1
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