metadata

base_model: sentence-transformers/all-MiniLM-L6-v2
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:59616
  - loss:CosineSimilarityLoss
widget:
  - source_sentence: >-
      We all need to do our part in slowing the spread of COVID-19 and practice
      social distancing. As a result, we'll be closing our retail store at 401
      Richmond until the end of March. You can still shop online at
      https://t.co/D5X7rxW0CS. Take care and stay safe everyone.
    sentences:
      - >-
        Retailers step up to fight coronavirus | Chain Store Age #Retail
        #Walmart #Pittsburgh #coronavirus #coronapocolypse 
        https://t.co/trV5Gzu2Gk
      - >-
        @TODAYshow Consumer reporter Vicky Ngyuen IS NOT qualified to answer
        medical questions, from Facebook or anywhere, regarding the #coronavirus
        #JustSayin
      - >-
        please help us stock some food coz we are in danger of this near by
        crisis of covid 19 which is now in our neighbouring country Kenya though
        we are just requesting to anyone who can manage to help us with anything
        small coz we want to stock food at the orphanage
  - source_sentence: "Joining hands with the UAE's efforts to contain the spread of the coronavirus (COVID-19) and considering the well-being of our mall visitors and community, gym, play areas &amp; amusement centres will stay closed.\r\r\n\r\r\nLuLu Hypermarket and other stores are operating in routine. https://t.co/SVOTBu6ZNu"
    sentences:
      - >-
        A supermarket in La Habra is trying to help local seniors during the
        COVID-19 pandemic by opening its doors a half-hour early each day
        exclusively for shoppers 65 and older.? https://t.co/U3An6bnU5L
      - "Response to COVID-19 (Novel Coronavirus)\r\r\n\r\r\nPost Winery\x92s retail store, including The Trellis Room, will be CLOSED to the public Monday, March 16. Further closure or limited hours will be announced the following week, Mon.Mar.23rd. POST Wine available at local stores. Be well? https://t.co/e9WSaIP3mf"
      - "Look where somebody #Parked at #Safeway here at #Oakland \r\r\n#ParkingLot #lol #coronavirus #stockup #apocalypse2020 https://t.co/oorfCF2I7T"
  - source_sentence: >-
      You know it's the end of the world when @BootsUK Surgical Spirit is out of
      stock online and (won't be receiving any further stock) - How am I
      supposed to disinfect my hands now ? #coronavirus #coronapocolypse #panic
      #shopping https://t.co/iQIpUz50Sc
    sentences:
      - "I\x92ve been online shopping for the past 2 days now #coronavirus"
      - "Mum actually mounted a dispenser and soap. You wash your hand before you enter her supermarket. \r\r\n\r\r\nNo chance for Covid-19"
      - >-
        Australia will NOT run out of food I get that people are fearful but
        panic buying creates more anxiety including for others who can t get
        what they actually need Please be kind to each other including shop
        staff If we work together the outcome is much better 19
  - source_sentence: >-
      To help you eat well and limit your movement during the #coronavirus
      crisis, you can ?download #applications for smartphones that make it
      easier to recover food at any time:?Find a restaurant or grocery store
      near you that can deliver healthy meals to your home.
    sentences:
      - "Was at the supermarket today. Didn't buy toilet paper. #Rebel\r\r\n\r\r\n#toiletpapercrisis #covid_19 https://t.co/eVXkQLIdAZ"
      - >-
        Are we getting quarantined an shoukd I stock up on food #coronavirus
        #Covid19Walkout #housearrest
      - "The opportunities &amp; challenges caused by #reverselogistics are even more apparent with the current #COVID-19 pandemic. As many stores have closed their physical locations, even more consumers are turning to online shopping. https://t.co/jmewQM90Z7\r\r\n#ECommerce #Returns #Coronavirus https://t.co/CCVKSzz3OH"
  - source_sentence: >-
      Governor Cuomo Please order retail businesses to close My husband has to
      go to work and expose himself to the virus Store owners will not close
      unless you tell them they have to I have two friends fighting for their
      lives PLEASE CLOSE STORES NOW
    sentences:
      - "Went to the store today. Most canned food is gone. We needed toilet paper and actually got a pack of 12 rolls. We got milk and cat treats as well. Thank god for Save on Foods for still having some stuff. \r\r\nOver 100 cases of Covid-19 in my province so panic buying is common."
      - "Clothier @LandsEnd has closed all of its retail stores through March 29th due to coronavirus pandemic.   They will pay all of their employees during the time period.\r\r\n\r\r\nThis is their store at the Hunt Valley Towne Center.  #HuntValley #coronavirus https://t.co/DIwNzAOLeb"
      - >-
        COVID-19: No Need For Panic Buying, Food Available At All Times - PM
        Assures https://t.co/K2ECAO51yA

SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2

This is a sentence-transformers model finetuned from sentence-transformers/all-MiniLM-L6-v2. It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

Model Details

Finetuning

The model was finetuned on Coronavirus tweets NLP - Text Classification. The objective was agrupate tweets by sentiment, for this, the dataset was divided into pairs of tweets and the similarity between them defined by the sentiment label. The following labels were used:

Extremely Positive and Extremely Negative: 0.0
Extremely Positive and Neutral: 0.2
Extremely Positive and Positive: 0.7
Extremely Positive and Negative: 0.2
Extremely Negative and Neutral: 0.2
Extremely Negative and Positive: 0.2
Extremely Negative and Negative: 0.7
Neutral and Positive: 0.5
Neutral and Negative: 0.5

Model Description

Model Type: Sentence Transformer
Base model: sentence-transformers/all-MiniLM-L6-v2
Maximum Sequence Length: 256 tokens
Output Dimensionality: 384 tokens
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': 256, 'do_lower_case': False}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 384, '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("gianvr/sbert-tunned-covid")
# Run inference
sentences = [
    'Governor Cuomo Please order retail businesses to close My husband has to go to work and expose himself to the virus Store owners will not close unless you tell them they have to I have two friends fighting for their lives PLEASE CLOSE STORES NOW',
    'Clothier @LandsEnd has closed all of its retail stores through March 29th due to coronavirus pandemic.   They will pay all of their employees during the time period.\r\r\n\r\r\nThis is their store at the Hunt Valley Towne Center.  #HuntValley #coronavirus https://t.co/DIwNzAOLeb',
    'Went to the store today. Most canned food is gone. We needed toilet paper and actually got a pack of 12 rolls. We got milk and cat treats as well. Thank god for Save on Foods for still having some stuff. \r\r\nOver 100 cases of Covid-19 in my province so panic buying is common.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]

Training Details

Training Dataset

Coronavirus tweets NLP - Text Classification

Size: 59,616 training samples
Columns: sentence1, sentence2, and label
Approximate statistics based on the first 1000 samples:
sentence1 sentence2 label
type string string float
details
min: 11 tokens
mean: 60.62 tokens
max: 245 tokens

min: 6 tokens
mean: 58.31 tokens
max: 245 tokens

min: 0.0
mean: 0.36
max: 0.7

	sentence1	sentence2	label
type	string	string	float
details	min: 11 tokens mean: 60.62 tokens max: 245 tokens	min: 6 tokens mean: 58.31 tokens max: 245 tokens	min: 0.0 mean: 0.36 max: 0.7

Samples:

sentence1	sentence2	label
`BBC Business correspondent just now: "We're going to see a sustained demand for food" Pretty sure that's not news?#CoronaCrisis #covid19uk`	`A warning from Nick Talley, University of Newcastle professor and neuro-gastroenterologist, for those who need to hear it: https://t.co/UzRRswUSTS`	`0.7`
`The announcement(over the PA)at #Woolworths #Marrickville Metro supermarket telling shoppers to maintain #distancing is absurd. In a supermarket aisle there just isn't enough room! #Auspol #NSWpol #coronavirus #Covid_19australia #COVID19Aus`	`Due to the closure of indoor shopping malls to prevent the spread of our Brooklyn Atlantic Office will be closed until further notice We apologize for the inconvenience Please visit our website to find more than 60 online transactions`	`0.5`
`Expert s top tips for supermarket shopping 1 Avoid cash 2 Use a key to type in credit card pin 3 Don t take a receipt 4 Use hand sanitizer once back at your car 5 Consider quarantining food Read more`	`Covid-19 is likely to disrupt these data in the coming months. So far, there is only limited evidence of surging demand in supermarkets stoking prices (mostly non-food and bread). Meanwhile, fruit, veg, and sugary item prices have all crashed. 2/3`	`0.2`

Loss: CosineSimilarityLoss with these parameters:

{
    "loss_fct": "torch.nn.modules.loss.MSELoss"
}

Training Hyperparameters

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: no
prediction_loss_only: True
per_device_train_batch_size: 8
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: 5e-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: -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: False
hub_always_push: False
gradient_checkpointing: False
gradient_checkpointing_kwargs: None
include_inputs_for_metrics: False
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
eval_use_gather_object: False
batch_sampler: batch_sampler
multi_dataset_batch_sampler: proportional

Training Logs

Epoch	Step	Training Loss
0.0671	500	0.1053
0.1342	1000	0.0802
0.2013	1500	0.0741
0.2684	2000	0.0701
0.3355	2500	0.0682
0.4026	3000	0.0665
0.4697	3500	0.0674
0.5368	4000	0.0667
0.6039	4500	0.0624
0.6710	5000	0.0613
0.7381	5500	0.0589
0.8052	6000	0.0647
0.8722	6500	0.0645
0.9393	7000	0.0626
1.0064	7500	0.0616
1.0735	8000	0.0591
1.1406	8500	0.0601
1.2077	9000	0.0592
1.2748	9500	0.0562
1.3419	10000	0.0586
1.4090	10500	0.0593
1.4761	11000	0.0578
1.5432	11500	0.0574
1.6103	12000	0.0561
1.6774	12500	0.0579
1.7445	13000	0.0556
1.8116	13500	0.0551
1.8787	14000	0.0554
1.9458	14500	0.0537
2.0129	15000	0.0548
2.0800	15500	0.0521
2.1471	16000	0.0534
2.2142	16500	0.0529
2.2813	17000	0.0529
2.3484	17500	0.052
2.4155	18000	0.0527
2.4826	18500	0.0517
2.5497	19000	0.0517
2.6167	19500	0.0497
2.6838	20000	0.0501
2.7509	20500	0.0516
2.8180	21000	0.0515
2.8851	21500	0.0511
2.9522	22000	0.0489

Framework Versions

Python: 3.10.12
Sentence Transformers: 3.2.1
Transformers: 4.44.2
PyTorch: 2.5.0+cu121
Accelerate: 0.34.2
Datasets: 3.1.0
Tokenizers: 0.19.1

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",
}