SentenceTransformer based on sentence-transformers/all-MiniLM-L12-v2

This is a sentence-transformers model finetuned from sentence-transformers/all-MiniLM-L12-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

Model Description

Model Type: Sentence Transformer
Base model: sentence-transformers/all-MiniLM-L12-v2
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.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("Trelis/all-MiniLM-L12-v2-ft-pairs-cosine")
# Run inference
sentences = [
    'Can an attacking player pass the ball into a defending player intentionally seeking a rebound or a restart of the Touch count?',
    ' a player enters the Field of Play but does not impede the scoring of a Try the \noffending player will be sent to the Sin Bin.\n17.8\tFollowing a Try, players may Interchange at will, without having to wait for the \nplayer to enter the Interchange Area, but must do so prior to the Tap being taken \nto recommence play.\n18\u2002 Penalty  \n18.1\tThe Tap must be performed in accordance with the Definitions.\nRuling = The Referee will instruct the offending Team to return to the Mark and perform the \nTap again.\n18.2\tFor Infringements that occur between seven (7) metre lines, the Mark for the \nPenalty Tap is at the point of Infringement unless otherwise indicated by the \nReferee. \n18.3\tFor Infringements that occur within the Seven Metre Zone the Tap must be \ntaken at the nearest seven (7) metre line.\n18.4\tFor Infringements that occur beyond the Field of Play or in the In-Goal Area \nthe Mark is seven (7) metres infield from the Sideline, or directly Forward of \nthe Infringement on the seven (7) metre line nearest the Infringement or at a \nposition indicated by the Referee.\n18.5\tThe Mark must be indicated by the Referee before a Penalty Tap is taken.\n18.6\tThe Penalty Tap must be performed without delay after the Referee indicates \nthe Mark.\nRuling = A Penalty to the non-offending team at the point of Infringement.\n18.7\tA player may perform a Rollball instead of a Penalty Tap and the player who \nreceives the ball does not become the Half.\n18.8\tIf the Defending Team is penalised three (3) times upon entering their Seven \nMetre Zone during a single Possession, the last offending player will be given an \nExclusion until the end of that Possession.\n18.9\tA Penalty Try is awarded if any action by a player, Team official or spectator, \ndeemed by the Referee to be contrary to the Rules or spirit of the game clearly \nprevents the Attacking Team from scoring a Try.\nFIT Playing Rules - 5th Edition\nCOPYRIGHT © Touch Football Australia 2020\n15\n19\u2002 Advantage  \n19.1\tWhere a Defending Team player is Offside at a Tap or',
    ' without delay.\nRuling = A Penalty to the non-offending team at the centre of the Halfway line.\n8\u2002 Match Duration  \n  \n8.1\tA match is 40 minutes in duration, consisting of two (2) x 20 minute halves with \na Half Time break.\n8.1.1\tThere is no time off for injury during a match.\n8.2\tLocal competition and tournament conditions may vary the duration of a match.\n8.3\tWhen time expires, play is to continue until the next Touch or Dead Ball and End \nof Play is signaled by the Referee. \n8.3.1\tShould a Penalty be awarded during this period, the Penalty is to be taken.\n8.4\tIf a match is abandoned in any circumstances other than those referred to in \nclause 24.1.6 the NTA or NTA competition provider in its sole discretion shall \ndetermine the result of the match.\n9\u2002 Possession  \n  \n9.1\tThe Team with the ball is entitled to six (6) Touches prior to a Change of \nPossession. \n9.2\tOn the Change of Possession due to an intercept, the first Touch will be zero (0) \nTouch. \n9.3\tFollowing the sixth Touch or a loss of Possession due to any other means, the \nball must be returned to the Mark without delay.\nRuling = A deliberate delay in the changeover procedure will result in a Penalty awarded to \nthe non-offending Team ten (10) metres Forward of the Mark for the Change of Possession.\n9.4\tIf the ball is dropped or passed and goes to ground during play, a Change of \nPossession results.\nRuling = The Mark for the Change of Possession is where the ball makes initial contact  with \nthe ground.\n9.5\tIf the ball, while still under the control of the Half, contacts the ground in the In-\nGoal Area, Possession is lost.\nRuling = Play will restart with a Rollball at the nearest point on the seven (7) metre line.\nFIT Playing Rules - 5th Edition\n8\nCOPYRIGHT © Touch Football Australia 2020\n9.6\tIf a player mishandles the ball and even if in an effort to gain control, the ball \nis accidentally knocked Forward into any other Player, a Change of Possession \nresults.\n10\u2002 The Touch  \n10.1\tA Touch may be made',
]
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 Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 32
per_device_eval_batch_size: 32
learning_rate: 1e-05
num_train_epochs: 1
lr_scheduler_type: cosine
warmup_ratio: 0.3
bf16: 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: 32
per_gpu_train_batch_size: None
per_gpu_eval_batch_size: None
gradient_accumulation_steps: 1
eval_accumulation_steps: None
learning_rate: 1e-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: cosine
lr_scheduler_kwargs: {}
warmup_ratio: 0.3
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: 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
0.1364	6	0.1722	0.1476
0.2727	12	0.1233	0.0820
0.4091	18	0.0702	0.0579
0.5455	24	0.0668	0.0593
0.6818	30	0.0527	0.0575
0.8182	36	0.0534	0.0561
0.9545	42	0.0659	0.0556

Framework Versions

Python: 3.10.12
Sentence Transformers: 3.0.1
Transformers: 4.41.2
PyTorch: 2.1.1+cu121
Accelerate: 0.31.0
Datasets: 2.17.1
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",
}

Trelis
/

all-MiniLM-L12-v2-ft-pairs-cosine