This is a sentence-transformers model finetuned from marrodion/minilm-l12-v2-simple. 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

Model Description

Model Type: Sentence Transformer
Base model: marrodion/minilm-l12-v2-simple
Maximum Sequence Length: 128 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': 128, '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.

python

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("marrodion/minilm-l12-v2-toxic-ft")
# Run inference
sentences = [
    'eradicate the gays',
    'Anyone who supports ISIS terrorism globally should be eradicated.',
    'This movie was filled with garbage Zionist propaganda.',
]
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]

Evaluation

Metrics

Semantic Similarity

Dataset: hatespeech-sampled-dev
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.5825
spearman_cosine	0.4527
pearson_manhattan	0.5684
spearman_manhattan	0.4501
pearson_euclidean	0.57
spearman_euclidean	0.4527
pearson_dot	0.5825
spearman_dot	0.4527
pearson_max	0.5825
spearman_max	0.4527

Training Details

Training Dataset

Unnamed Dataset

Size: 33,827 training samples
Columns: sentence1, sentence2, and score
Approximate statistics based on the first 1000 samples:
sentence1 sentence2 score
type string string float
details
min: 4 tokens
mean: 35.5 tokens
max: 128 tokens
min: 4 tokens
mean: 34.35 tokens
max: 128 tokens
min: 0.0
mean: 0.82
max: 1.0

Samples:

sentence1	sentence2	score
`Don't waste good pussy on a trash ass nigga`	`Stfu obviously trans people aren't retarded`	`0.7777777777777778`
`The more I read about dumb violent things do, like beating up two women bc they wouldn't kiss for them.. the gayer I become #Pride2019`	`Nuke them all`	`0.5555555555555556`
`Forget her cunt and ass and fuck her face till she pukes!`	`@QTAnon1 Hope she fucks her all the way back to Somalia..`	`1.0`

Loss: CosineSimilarityLoss with these parameters:
json
1{ 2 "loss_fct": "torch.nn.modules.loss.MSELoss" 3}

Evaluation Dataset

Unnamed Dataset

Size: 1,781 evaluation samples
Columns: sentence1, sentence2, and score
Approximate statistics based on the first 1000 samples:
sentence1 sentence2 score
type string string float
details
min: 4 tokens
mean: 35.77 tokens
max: 128 tokens
min: 4 tokens
mean: 36.67 tokens
max: 128 tokens
min: 0.11
mean: 0.83
max: 1.0

Samples:

sentence1	sentence2	score
`So for Trump and his inner circle to be crying over law and order, they sure know how to follow the system huh? Yet nothing about his ways of following the law were legal. So, how is his asylum rules were ever legal if children were sick and forced to live in unconditioned areas is American or right?`	`We need every illegal alien gang member in prison. Then deported.`	`1.0`
`hey girl are you my dick cause im about to beat the shit out of you`	`Reading all these hot takes from Americans, they all just seem like utter dogs. A human mind should not be this deranged, yet this is the norm in America`	`0.8888888888888888`
`Its amazing what a failure women are in politics. Its almost like its not in their nature to be powerful. No matter how much they try, poor dears....`	`Tbh that cut makes him look like neonazi. He looks like he's about to lecture me about how white people are the most discriminates against racially after he called me the n word with that hard r.`	`0.8888888888888888`

Loss: CosineSimilarityLoss with these parameters:
json
1{ 2 "loss_fct": "torch.nn.modules.loss.MSELoss" 3}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 32
per_device_eval_batch_size: 64
num_train_epochs: 1
warmup_ratio: 0.1
load_best_model_at_end: True

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: steps
prediction_loss_only: True
per_device_train_batch_size: 32
per_device_eval_batch_size: 64
per_gpu_train_batch_size: None
per_gpu_eval_batch_size: None
gradient_accumulation_steps: 1
eval_accumulation_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: 1
max_steps: -1
lr_scheduler_type: linear
lr_scheduler_kwargs: {}
warmup_ratio: 0.1
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: True
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
batch_sampler: batch_sampler
multi_dataset_batch_sampler: proportional

Training Logs

Epoch	Step	Training Loss	loss	hatespeech-sampled-dev_spearman_cosine
0.2836	300	0.0503	0.0139	0.4258
0.5671	600	0.0143	0.0135	0.4418
0.8507	900	0.0134	0.0131	0.4527

The bold row denotes the saved checkpoint.

Framework Versions

Python: 3.10.14
Sentence Transformers: 3.0.0
Transformers: 4.41.1
PyTorch: 2.3.0
Accelerate: 0.30.1
Datasets: 2.19.1
Tokenizers: 0.19.1

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",
}

	sentence1	sentence2	score
type	string	string	float
details	min: 4 tokens mean: 35.5 tokens max: 128 tokens	min: 4 tokens mean: 34.35 tokens max: 128 tokens	min: 0.0 mean: 0.82 max: 1.0