This is a sentence-transformers model finetuned from NovaSearch/stella_en_400M_v5 on the csv dataset. 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: NovaSearch/stella_en_400M_v5
Maximum Sequence Length: 512 tokens
Output Dimensionality: 1024 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': 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): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
)

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("Bharatdeep-H/stella_finetuned_en_dataset_to_mine_negatives_from")
# Run inference
sentences = [
    'day I was acquitted 12/12/12 i hocus45th GP SERVICES USA CDC CENTERS FOR DISH CONTROL AND P EXCLUSIVE: Per the CDC There Are Nearly Twice As Many Vaccine Related Deaths SO FAR in 2021 (1,755) Than All the Vaccine Deaths this Past Decade (994) For information about vaccines. visit who.int.',
    '“Thousands of COVID Vaccine Injuries and 13 U.S. Deaths Reported in December Alone”; “In December, 3,916 COVID vaccine-related adverse events, including 13 deaths, were reported to VAERS”',
    'Side effects of the first published vaccine According to Pfizer documents, 1,200 deaths.',
]
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]

Evaluation

Metrics

Triplet

Evaluated with TripletEvaluator

Metric	Value
cosine_accuracy	0.9714

Training Details

Training Dataset

csv

Dataset: csv
Size: 164,619 training samples
Columns: query, answer, and negative
Approximate statistics based on the first 1000 samples:
query answer negative
type string string string
details
min: 5 tokens
mean: 83.66 tokens
max: 512 tokens
min: 6 tokens
mean: 19.17 tokens
max: 141 tokens
min: 6 tokens
mean: 18.69 tokens
max: 120 tokens

Samples:

query	answer	negative
`YEAST EXTRACK C DDees.co NO AFTER 40 YEARS OF RESEARCH, THERE IS A VACCINE AGAINST AIDS AFTER 76 YEARS OF RESEARCH, NO THERE IS A VACCINE AGAINST INFLUENZA AFTER 100 YEARS OF RESEARCH, NO THERE IS A VACCINE AGAINST CANCER BUT AFTER ONLY 6 MONTHS THERE IS A VACCINE AGAINST A "VIRUS" THAT APPEARED "FROM SUDDEN" AND THOSE WHO`	`After years of research there is no vaccine against AIDS, cancer and influenza, but in 6 months there is a vaccine against covid-19`	`Chronology of decline in vaccine effectiveness shows that immunizers are not effective`
`Donald J. Trump [USER] Trump We have reports of NIGERIA supporting Iran ACTIVELY and PASSIVELY....just got of the phone with secretary Pompeo....the USA MUST and WILL respond to terrorist nations IMMEDIATELY! 12:11 AM - 08 Jan 2020 244,391 Retweets 319,284 Likes 167K [244K Follow 319K`	`US President Donald Trump threatens to attack Nigeria`	`Vice President of the United States Mike Pence unfollowed President Donald Trump on Twitter and put a picture of Joe Biden and Kamala Harris on the profile cover`
`4 k 40 years worth of research... *no vaccine for HIV cancer MA At least 100 years of research...no vaccine for Ongoing research... no vaccine for the common cold Less than a year for a Covid vaccine? I think I'll pass on that shot!! PIC COLLAGE`	`Vaccines for HIV, cold and cancer should deter you from getting the Covid-19 vaccine`	`Anticovid vaccines cause deterioration of the immune system and AIDS`

Loss: TripletLoss with these parameters:
json
1{ 2 "distance_metric": "TripletDistanceMetric.EUCLIDEAN", 3 "triplet_margin": 5 4}

Evaluation Dataset

csv

Dataset: csv
Size: 164,619 evaluation samples
Columns: query, answer, and negative
Approximate statistics based on the first 1000 samples:
query answer negative
type string string string
details
min: 5 tokens
mean: 73.2 tokens
max: 512 tokens
min: 5 tokens
mean: 19.27 tokens
max: 103 tokens
min: 5 tokens
mean: 19.3 tokens
max: 179 tokens

Samples:

query	answer	negative
`--- Nora Al Riyadh Tik Tok Replying to [USER]`	`A restaurant in Riyadh in one of the malls, the opening of the meal is free, of course, Corona in the farthest corner is surprising`	`Video of Rakhi Sawant wearing a hijab to support protestors in Karnataka`
`SQUID FOR BRAZIL BELTER SALT Corumbau pasil Milk`	`Lula expelled from the city of Itanagrà in Bahia in May 2021. The Army had to provide security.`	`All workers in Gardenia Philippines bread factory COVID-19 positive in July 2020`
`I just ran out of words William Barr, Attorney General of the America literally most important person of all the American court system just publicly denounced that there has been electoral fraud 2rad10 TM [USER].6h US Attorney General William Barr denounces Vote-by-mail fraud. OM BLITZER [USER] THE WITH THE WITH SITUATION WOLFOOTION WOLF S BLITZE ROOM OUTZER TH HE WITH DERNIE CNN EXCLUSIVE Jimmy Carter & James Baker WOLF ONE-ON-ONE WITH ATTORNEY GENERAL WILLIAM BARR CAN WELL DEEST`	`The US attorney general denounces that there has been electoral fraud`	`Hillary Clinton appeared before the US justice on June 2, 2020`

Loss: TripletLoss with these parameters:
json
1{ 2 "distance_metric": "TripletDistanceMetric.EUCLIDEAN", 3 "triplet_margin": 5 4}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 2
learning_rate: 3e-05
max_steps: 4000
lr_scheduler_type: cosine
warmup_ratio: 0.2
bf16: True
batch_sampler: no_duplicates

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: steps
prediction_loss_only: True
per_device_train_batch_size: 2
per_device_eval_batch_size: 8
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: 3e-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: 3.0
max_steps: 4000
lr_scheduler_type: cosine
lr_scheduler_kwargs: {}
warmup_ratio: 0.2
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: True
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: no_duplicates
multi_dataset_batch_sampler: proportional

Training Logs

Epoch	Step	Training Loss	Validation Loss	cosine_accuracy
0.0016	100	1.6958	-	-
0.0032	200	1.3647	-	-
0.0049	300	1.1698	-	-
0.0065	400	0.8551	-	-
0.0081	500	0.8275	-	-
0.0097	600	0.8878	-	-
0.0113	700	0.9717	-	-
0.0130	800	1.0219	-	-
0.0146	900	0.9074	-	-
0.0162	1000	0.903	0.8201	0.9452
0.0178	1100	0.9236	-	-
0.0194	1200	0.7935	-	-
0.0211	1300	1.0483	-	-
0.0227	1400	1.0878	-	-
0.0243	1500	0.9258	-	-
0.0259	1600	1.011	-	-
0.0275	1700	0.7785	-	-
0.0292	1800	0.7643	-	-
0.0308	1900	0.9918	-	-
0.0324	2000	0.7941	0.7678	0.9387
0.0340	2100	1.106	-	-
0.0356	2200	0.7571	-	-
0.0373	2300	0.6687	-	-
0.0389	2400	0.6914	-	-
0.0405	2500	0.5925	-	-
0.0421	2600	0.8085	-	-
0.0437	2700	0.5775	-	-
0.0454	2800	0.5051	-	-
0.0470	2900	0.6894	-	-
0.0486	3000	0.4202	0.4875	0.9667
0.0502	3100	0.4704	-	-
0.0518	3200	0.4511	-	-
0.0535	3300	0.3991	-	-
0.0551	3400	0.4166	-	-
0.0567	3500	0.3402	-	-
0.0583	3600	0.6621	-	-
0.0599	3700	0.5999	-	-
0.0616	3800	0.443	-	-
0.0632	3900	0.6503	-	-
0.0648	4000	0.42	0.4156	0.9714

Framework Versions

Python: 3.10.16
Sentence Transformers: 3.3.1
Transformers: 4.49.0
PyTorch: 2.5.1+cu121
Accelerate: 1.4.0
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",
}

TripletLoss

bibtex

@misc{hermans2017defense,
    title={In Defense of the Triplet Loss for Person Re-Identification},
    author={Alexander Hermans and Lucas Beyer and Bastian Leibe},
    year={2017},
    eprint={1703.07737},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}

	query	answer	negative
type	string	string	string
details	min: 5 tokens mean: 83.66 tokens max: 512 tokens	min: 6 tokens mean: 19.17 tokens max: 141 tokens	min: 6 tokens mean: 18.69 tokens max: 120 tokens