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tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:21769
  - loss:MultipleNegativesRankingLoss
base_model: am-azadi/bilingual-embedding-large_Fine_Tuned_1e
widget:
  - source_sentence: >-
      GOOD NEWS!  Eriksen, has already gone out to the hospital window, where he
      is under observation and looks optimistic after having suffered a cardiac
      arrest. 
    sentences:
      - >-
        Bolsonaro with the two assassins of Marielle Franco No, the men next to
        Jair Bolsonaro in this photo are not the ones accused of the murder of
        Marielle Franco
      - >-
        This photo shows Christian Eriksen waving from the window of the
        hospital where he was admitted after suffering cardiac arrest The photo
        of Eriksen waving from the window was taken months before his heart
        incident
      - >-
        Video of protests in the US during the COVID-19 pandemic This video has
        been circulating in reports about the funeral procession of military
        commanders in Iran in January 2020
  - source_sentence: >-
      What a dirty game... "US postman arrested in canadian border with
      banknotes stolen in the trunk of the car". 91 Breaking911  5h U.S. Postal
      Worker Caught at Canadian Border With Stolen Ballots In Car Trunk -
      breaking911.com/u-s-postal-wor... 8218248 Claudia Wild   IT 8206434 300  
      4:57 06 Nov 20 Twitter for iPhone 1,134 Retweets 113 Tweets with comment
    sentences:
      - >-
        Postman arrested with stolen bills at US-Canada border Only three blank
        bills were found in a US postal worker's car
      - >-
        Covid relief plan will cost every American $5,750 Misleading posts claim
        US covid relief plan costs every American $5,750
      - >-
        CDC informs that 10% of the swabs used for PCR testing were sent to
        LABORATORIES, being analyzed of GENETIC SEQUENCES We check the claim
        that PCR tests aim to sequence the DNA of patients with covid-19
  - source_sentence: >-
      . Northeast Always in Our Hearts! Advance Northeast!!  . Brazilian Army
      through its Engineering Battalion finds a Huge Potable Water Well in
      Seridó - Caicó/RN, one of the most needy areas. This well will supply the
      homes of more than 3,000 people!!  . It's our President Bolsonaro ridding
      the Bravo People of the Northeast from the wounds of drought! .  . . 
      BRAZIL LOVED HOMELAND  . . Friends and Followers of : Follow and Turn on
      our Notifications  . .          # pocket                  . 
    sentences:
      - >-
        Twitter suspended Elon Musk's Twitter account after he pulled out of
        deal Imposter Elon Musk Twitter account shared in false posts claiming
        he was 'suspended' over buyout row
      - >-
        The Brazilian Army found water in Caicó, Rio Grande do Norte, during the
        government of President Jair Bolsonaro. The recording of the drilling of
        an artesian well in Caicó, Rio Grande do Norte, has been circulating
        since 2015
      - >-
        A video was published today about Syrian refugees in Sweden being
        subjected to the separation of husbands, as well as the forcible removal
        of their children and the handing over of children to Christian families
        to change their religion. And to turn them into Christians, they will
        have two children Swedish police did not take Syrian children to hand
        over to Christian families
  - source_sentence: >-
      what hp Álvaro Uribe Vélez ... 3pm ✓ The coastal people are the least
      intellectual of the country, that is why this region of Colombia is mired
      in poverty. They don't like to work either. that's why there is currently
      a level very high of misery in la guajira. With the democratic center we
      will change. The entire Caribbean coast must feel outraged by the
      statements of this individual. Now with more reasons, the coastal people
      should support Petro. The how.. see more
    sentences:
      - >-
        Covid-19: Omicron variant is transmitted by eye contact according to the
        WHO The coronavirus is transmitted by interaction with contaminated
        droplets, not by eye contact
      - >-
        5G causes suffocation in humans, affects the respiratory system There is
        no evidence that 5G technology affects the respiratory system and
        increases toxins in the body
      - >-
        Álvaro Uribe tweeted that the coastal people are the least intellectual
        population in Colombia There is no record of Uribe tweeting that the
        coast is the "least intellectual" region of Colombia
  - source_sentence: >-
      The terrorists evaporated in seconds  A very rare scene of the moment the
      Egyptian planes bombed the terrorist elements in Sinai Watch the video
      here   NB Please all our followers on our page subscribe to our YouTube
      channel We will publish everything new on the ground Open the channel
      link 
    sentences:
      - >-
        Cars melt due to hot weather in Saudi Arabia No, these cars did not melt
        due to hot weather
      - >-
        Footage shows robbery in Sri Lanka Delhi crime footage falsely shared as
        'Sri Lanka burglary'
      - >-
        A very rare scene of the moment the Egyptian planes bombed the terrorist
        elements in Sinai This picture is not of an Egyptian warplane, but of an
        Israeli plane
pipeline_tag: sentence-similarity
library_name: sentence-transformers

SentenceTransformer based on am-azadi/bilingual-embedding-large_Fine_Tuned_1e

This is a sentence-transformers model finetuned from am-azadi/bilingual-embedding-large_Fine_Tuned_1e. 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/bilingual-embedding-large_Fine_Tuned_1e
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: BilingualModel 
  (1): Pooling({'word_embedding_dimension': 1024, '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})
  (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 = [
    'The terrorists evaporated in seconds  A very rare scene of the moment the Egyptian planes bombed the terrorist elements in Sinai Watch the video here   NB Please all our followers on our page subscribe to our YouTube channel We will publish everything new on the ground Open the channel link ',
    'A very rare scene of the moment the Egyptian planes bombed the terrorist elements in Sinai This picture is not of an Egyptian warplane, but of an Israeli plane',
    'Cars melt due to hot weather in Saudi Arabia No, these cars did not melt due to hot weather',
]
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: 21,769 training samples
Columns: sentence_0 and sentence_1
Approximate statistics based on the first 1000 samples:
sentence_0 sentence_1
type string string
details
min: 6 tokens
mean: 119.28 tokens
max: 512 tokens

min: 18 tokens
mean: 39.42 tokens
max: 98 tokens

	sentence_0	sentence_1
type	string	string
details	min: 6 tokens mean: 119.28 tokens max: 512 tokens	min: 18 tokens mean: 39.42 tokens max: 98 tokens

Samples:

sentence_0	sentence_1
`HAPPENING NOW ; KENYA ELECTRIC BUS IS ON FIRE ALONG KAREN ROAD.`	`Electric bus catches fire in Nairobi Video shows a methane-powered bus that caught fire in Italy, not an electric bus in Kenya`
`RUPTLY Viewed 51,670 times 8 hours Snorr On the way down Khao Pak Thong Chai, route 3-4, Sattahip - Korat, all of them would have died. pity Incident 27 Jun.`	`Video showing road accidents in Thailand? This is a video published in a news report about a car crash in Russia.`
The image that went around the world! This photo won the best of the decade award and led to the author to depression, the author narrated in his description; "Cheetahs chased a mother deer and her 2 babies, she offered herself so that her children could escape and in the photo looks like she watches her babies run to safety as she is about to be devoured" How many times have you stopped to think how many sacrifices your parents do for you. While you have fun, laugh and you enjoy life, they give theirs.	`Cheetahs chased a mother deer and she volunteered so her children could escape Behind the picture: Cheetahs learned from their mother how to capture prey`

Loss: MultipleNegativesRankingLoss with these parameters:

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

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
0.0459	500	0.0135
0.0919	1000	0.024
0.1378	1500	0.0073
0.1837	2000	0.0103
0.2297	2500	0.0265
0.2756	3000	0.0209
0.3215	3500	0.0308
0.3675	4000	0.0301
0.4134	4500	0.0382
0.4593	5000	0.0164
0.5053	5500	0.0251
0.5512	6000	0.0141
0.5972	6500	0.0131
0.6431	7000	0.006
0.6890	7500	0.0261
0.7350	8000	0.0111
0.7809	8500	0.0089
0.8268	9000	0.0201
0.8728	9500	0.0175
0.9187	10000	0.0086
0.9646	10500	0.0049

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}
}