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tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - dense
  - generated_from_trainer
  - dataset_size:23522
  - loss:SplitHeadContrastiveDistillationLoss
base_model: microsoft/harrier-oss-v1-270m
widget:
  - source_sentence: >-
      Instruct: Retrieve text with a similar pragmatic profile, including
      safety, emotion, sentiment, language, and identity-target signals

      Query: "Since women say men only think with their dicks do you think she
      would get offended if I asked her to blow my mind." 😭 I hate the people I
      work with fucking clowns
    sentences:
      - >-
        Instruct: Retrieve text with a similar pragmatic profile, including
        safety, emotion, sentiment, language, and identity-target signals

        Query: /r/ENLIGHTENEDCENTRISM  Because someone who wants equality and a
        nazi are equally as bad, and homophobes have absolutely *no track
        record* of not letting gays keep practicing their ~~comedy~~ life. As
        opposed to SJWs who have gone into history responsible for villifying,
        suppressing and outright killing sexual minorities. But yeah no, middle
        ground all the way babyyy. You're the smartest guy on Reddit!
      - >-
        Instruct: Retrieve text with a similar pragmatic profile, including
        safety, emotion, sentiment, language, and identity-target signals

        Query: they do not care about me or you, they care about what they can
        take from you and what they can make you do for them.
      - >-
        Instruct: Retrieve text with a similar pragmatic profile, including
        safety, emotion, sentiment, language, and identity-target signals

        Query: @smkndofpnutdssr @ACLU 70 years ago everyone was brainwashed into
        being christian and also had coathanger abortions because it was the
        Great Depression and then thousands on women died because they had
        unsafe abortions 😍
  - source_sentence: >-
      Instruct: Retrieve text with a similar pragmatic profile, including
      safety, emotion, sentiment, language, and identity-target signals

      Query: Real estate is... volatile?


      Well first off, risk is not volatility, and they're not even related
      (yeah, you have to take uni-level finance courses to learn this...).  So
      don't conflate the two terms.


      Second, any dunce stupid enough to sell real estate for carry forward cap
      losses (you also do know that cap losses can only be applied against cap
      gains, yes?) is actually going to help bring liquidity into a market,
      right?
    sentences:
      - >-
        Instruct: Retrieve text with a similar pragmatic profile, including
        safety, emotion, sentiment, language, and identity-target signals

        Query: Islam is not a race, it's a religion, so we can't be racist
      - >-
        Instruct: Retrieve text with a similar pragmatic profile, including
        safety, emotion, sentiment, language, and identity-target signals

        Query: Really? Who else would ignore 97% of scientists on subject? That
        is willful ignorance. They don't and/or won't understand climate change.
        They're ignorance is going to kill us all. Including an ignorant
        president who thinks cc is a Chinese conspiracy.  Talk about arrogance.
        (and stupidity).
      - >-
        Instruct: Retrieve text with a similar pragmatic profile, including
        safety, emotion, sentiment, language, and identity-target signals

        Query: america is the best country in the world because we are free, and
        we are free because we are christian, and we are christian because
  - source_sentence: >-
      Instruct: Retrieve text with a similar pragmatic profile, including
      safety, emotion, sentiment, language, and identity-target signals

      Query: Nasty ass old white men. Send them all to prison right along with
      RKelly!!
    sentences:
      - >-
        Instruct: Retrieve text with a similar pragmatic profile, including
        safety, emotion, sentiment, language, and identity-target signals

        Query: the government should not be in the business of providing
        services for those who cannot help themselves
      - >-
        Instruct: Retrieve text with a similar pragmatic profile, including
        safety, emotion, sentiment, language, and identity-target signals

        Query: Isis they are not muslims, they are made of usa stop ur fucking
        shit 💩
      - >-
        Instruct: Retrieve text with a similar pragmatic profile, including
        safety, emotion, sentiment, language, and identity-target signals

        Query: He hasn't been effective since 2015. It's his only option other
        than retiring.
  - source_sentence: >-
      Instruct: Retrieve text with a similar pragmatic profile, including
      safety, emotion, sentiment, language, and identity-target signals

      Query: The writer was also upset that she, a mother of 3, didn't get a
      chance to eat that morning. So she wanted to punish the metro worker for
      eating????? This lawsuit is the most entitled pile of garbage. She's
      accusing the publisher of attacking her cos she's a woman of color URL
    sentences:
      - >-
        Instruct: Retrieve text with a similar pragmatic profile, including
        safety, emotion, sentiment, language, and identity-target signals

        Query: please shove all the nearest white shit in your mouth and
        swallow.. & not your homeboy, either. look past him.
      - >-
        Instruct: Retrieve text with a similar pragmatic profile, including
        safety, emotion, sentiment, language, and identity-target signals

        Query: And not one Representative would accept their version " health
        care" as their own.
      - >-
        Instruct: Retrieve text with a similar pragmatic profile, including
        safety, emotion, sentiment, language, and identity-target signals

        Query: This article is complete nonsense and those who buy into it need
        to get a real life.
  - source_sentence: >-
      Instruct: Retrieve text with a similar pragmatic profile, including
      safety, emotion, sentiment, language, and identity-target signals

      Query: Everyone in my country has been killing each other for years over
      religion and they're not even different religion just different branches
      of Christianity and I quickly realised it's all pointless
    sentences:
      - >-
        Instruct: Retrieve text with a similar pragmatic profile, including
        safety, emotion, sentiment, language, and identity-target signals

        Query: Yea I saw that...had me worried for a sec but I had no trouble at
        Delta
      - >-
        Instruct: Retrieve text with a similar pragmatic profile, including
        safety, emotion, sentiment, language, and identity-target signals

        Query: Me when my family confronts me about all the queer content on my
        social media URL
      - >-
        Instruct: Retrieve text with a similar pragmatic profile, including
        safety, emotion, sentiment, language, and identity-target signals

        Query: Good to see Tomas Rosicky playing tdae #ARSvQPR
pipeline_tag: sentence-similarity
library_name: sentence-transformers

SentenceTransformer based on microsoft/harrier-oss-v1-270m

This is a sentence-transformers model finetuned from microsoft/harrier-oss-v1-270m. It maps sentences & paragraphs to a 896-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: microsoft/harrier-oss-v1-270m
Maximum Sequence Length: 512 tokens
Output Dimensionality: 896 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, 'architecture': 'Gemma3TextModel'})
  (1): Pooling({'word_embedding_dimension': 640, 'pooling_mode_cls_token': False, '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': True, 'include_prompt': True})
  (2): Dense({'in_features': 640, 'out_features': 896, 'bias': False, 'activation_function': 'torch.nn.modules.linear.Identity'})
  (3): 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 = [
    "Instruct: Retrieve text with a similar pragmatic profile, including safety, emotion, sentiment, language, and identity-target signals\nQuery: Everyone in my country has been killing each other for years over religion and they're not even different religion just different branches of Christianity and I quickly realised it's all pointless",
    'Instruct: Retrieve text with a similar pragmatic profile, including safety, emotion, sentiment, language, and identity-target signals\nQuery: Me when my family confronts me about all the queer content on my social media URL',
    'Instruct: Retrieve text with a similar pragmatic profile, including safety, emotion, sentiment, language, and identity-target signals\nQuery: Good to see Tomas Rosicky playing tdae #ARSvQPR',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 896]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0078, 0.6172, 0.5234],
#         [0.6172, 1.0000, 0.5859],
#         [0.5234, 0.5859, 1.0000]], dtype=torch.bfloat16)

Training Details

Training Dataset

Unnamed Dataset

Size: 23,522 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: 31 tokens
mean: 60.22 tokens
max: 275 tokens

min: 30 tokens
mean: 59.05 tokens
max: 262 tokens

min: 0.0
mean: 0.5
max: 1.0

	sentence_0	sentence_1	label
type	string	string	float
details	min: 31 tokens mean: 60.22 tokens max: 275 tokens	min: 30 tokens mean: 59.05 tokens max: 262 tokens	min: 0.0 mean: 0.5 max: 1.0

Samples:

sentence_0	sentence_1	label
`Instruct: Retrieve text with a similar pragmatic profile, including safety, emotion, sentiment, language, and identity-target signals Query: money grubbing filthy kike in panic mode he has to refund shekels`	`Instruct: Retrieve text with a similar pragmatic profile, including safety, emotion, sentiment, language, and identity-target signals Query: the only ones that have a mental illness are the jews for reading a racial supremacy manifesto that says they are the master race jews always accuse non jews of everything that jews are guilty of`	`1.0`
`Instruct: Retrieve text with a similar pragmatic profile, including safety, emotion, sentiment, language, and identity-target signals Query: @user SJSHSJ THATS MY JOB BITCH`	`Instruct: Retrieve text with a similar pragmatic profile, including safety, emotion, sentiment, language, and identity-target signals Query: STOCKS RECORD HIGH URL #MAGA`	`0.0`
`Instruct: Retrieve text with a similar pragmatic profile, including safety, emotion, sentiment, language, and identity-target signals Query: the best way to do this is to keep them from coming to america, and the best way to keep them from coming to america is to`	`Instruct: Retrieve text with a similar pragmatic profile, including safety, emotion, sentiment, language, and identity-target signals Query: i have a friend who works in a restaurant and he says that he has never seen a white person working as a busboy or dishwasher`	`1.0`

Loss: main.SplitHeadContrastiveDistillationLoss

Training Hyperparameters

Non-Default Hyperparameters

per_device_train_batch_size: 16
per_device_eval_batch_size: 16
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: 16
per_device_eval_batch_size: 16
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: None
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
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}
parallelism_config: None
deepspeed: None
label_smoothing_factor: 0.0
optim: adamw_torch_fused
optim_args: None
adafactor: False
group_by_length: False
length_column_name: length
project: huggingface
trackio_space_id: trackio
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
hub_revision: None
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
include_tokens_per_second: False
include_num_input_tokens_seen: no
neftune_noise_alpha: None
optim_target_modules: None
batch_eval_metrics: False
eval_on_start: False
use_liger_kernel: False
liger_kernel_config: None
eval_use_gather_object: False
average_tokens_across_devices: True
prompts: None
batch_sampler: batch_sampler
multi_dataset_batch_sampler: round_robin
router_mapping: {}
learning_rate_mapping: {}

Training Logs

Epoch	Step	Training Loss
0.3399	500	0.0316
0.6798	1000	0.0315
1.0197	1500	0.031
1.3596	2000	0.0298
1.6995	2500	0.0302
0.3399	500	0.0288
0.6798	1000	0.029

Framework Versions

Python: 3.14.4
Sentence Transformers: 5.1.0
Transformers: 4.57.6
PyTorch: 2.11.0+cu128
Accelerate: 1.13.0
Datasets: 4.8.4
Tokenizers: 0.22.2

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",
}