MPNet base trained on AllNLI triplets

This is a sentence-transformers model finetuned from prajjwal1/bert-tiny on the pair_similarity dataset. It maps sentences & paragraphs to a 128-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: prajjwal1/bert-tiny
Maximum Sequence Length: 512 tokens
Output Dimensionality: 128 dimensions
Similarity Function: Cosine Similarity
Training Dataset:
- pair_similarity
Language: en
License: apache-2.0

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: BertModel 
  (1): Pooling({'word_embedding_dimension': 128, '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})
)

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("Tien09/tiny_bert_ft_sim_score")
# Run inference
sentences = [
    'If you control 3 or more face-up "Six Samurai" monsters, you can activate 1 of these effects: Destroy all face-up monsters your opponent controls. Destroy all face-up Spell/Trap Cards your opponent controls. Destroy all Set Spell/Trap Cards your opponent controls.',
    'Target 1 Link Monster you control and 1 monster your opponent controls; destroy them, then draw 1 card. You can only activate 1 "Link Burst" per turn.',
    'While you have 2 or less cards in your hand, all face-up "Fabled" monsters you control gain 400 ATK.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 128]

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

Training Details

Training Dataset

pair_similarity

Dataset: pair_similarity at a933de4
Size: 8,959 training samples
Columns: effect_text, score, and effect_text2
Approximate statistics based on the first 1000 samples:
effect_text score effect_text2
type string float string
details
min: 6 tokens
mean: 72.39 tokens
max: 191 tokens

min: 0.0
mean: 0.09
max: 1.0

min: 8 tokens
mean: 72.1 tokens
max: 198 tokens

Samples:

effect_text	score	effect_text2
`Once per turn, if you Special Summon a DARK Synchro Monster(s) from the Extra Deck: You can target 1 of your "Blackwing" monsters, or "Black-Winged Dragon", with lower ATK that is banished or in your GY; Special Summon it. Once per turn, if a DARK monster(s) you control would be destroyed by battle or card effect, you can remove 1 Black Feather Counter from your field instead.`	`0.0`	`A Millennium item, it's rumored to block any strong attack.`
`Target 1 face-up monster your opponent controls; the ATK of all other monsters currently on the field become equal to that monster's ATK, until the end of this turn.`	`0.0`	`While you control a "Blue-Eyes" monster, you choose the attack targets for your opponent's attacks. You can only use each of the following effects of "Dictator of D." once per turn. You can send 1 "Blue-Eyes White Dragon" from your hand or Deck to the GY; Special Summon this card from your hand. You can discard 1 "Blue-Eyes White Dragon", or 1 card that mentions it, then target 1 "Blue-Eyes" monster in your GY; Special Summon it.`
`1 Tuner + 1+ non-Tuner monsters`
If this card is Synchro Summoned using a Tuner Synchro Monster: You can target 1 Spell/Trap in your GY; add it to your hand. When your opponent activates a card or effect (Quick Effect): You can send 1 Spell/Trap from your hand or field to the GY; Special Summon 1 Level 7 or lower Tuner Synchro Monster from your Extra Deck, GY, or banishment. You can only use each effect of "Diabell, Queen of the White Forest" once per turn.	`0.2`	`1 Aqua monster + 1 Level 10 WATER monster Must first be either Fusion Summoned, or Special Summoned (from your Extra Deck) by Tributing 1 Level 10 Aqua monster with 0 ATK. This card can be treated as 3 Tributes for the Tribute Summon of a monster. Cannot be destroyed by battle. Your opponent cannot target monsters you control with card effects, except "Egyptian God Slime", also their monsters cannot target monsters for attacks, except "Egyptian God Slime".`

Loss: CoSENTLoss with these parameters:

{
    "scale": 20.0,
    "similarity_fct": "pairwise_cos_sim"
}

Evaluation Dataset

pair_similarity

Dataset: pair_similarity at a933de4
Size: 1,920 evaluation samples
Columns: effect_text, score, and effect_text2

Approximate statistics based on the first 1000 samples:

	effect_text	score	effect_text2
type	string	float	string
details	min: 10 tokens mean: 72.56 tokens max: 202 tokens	min: 0.0 mean: 0.09 max: 1.0	min: 5 tokens mean: 71.33 tokens max: 186 tokens

Samples:

effect_text	score	effect_text2
`A proud ruler of the jungle that some fear and others respect.`	`0.0`	`Cannot attack the turn it is Normal Summoned. Once per turn: You can target 1 face-up monster on the field; change this card to Defense Position, and if you do, that target loses 800 ATK until the end of this turn.`
`During your opponent's Main Phase or Battle Phase: You can Special Summon 1 non-Tuner monster from your hand, but it has its effects negated (if any), and if you do, immediately after this effect resolves, Synchro Summon 1 Machine-Type Synchro Monster using only that monster and this card (this is a Quick Effect). You can only use this effect of "Crystron Quan" once per turn.`	`0.0`	`You can Tribute this card while "Neo Space" is on the field to Special Summon 1 "Neo-Spacian Dark Panther" from your hand or Deck.`
`When your opponent Special Summons a monster(s): Destroy it, then you can banish 5 Zombie monsters from your GY, and if you do, Special Summon 1 Level 7 or higher Zombie monster from your hand or Deck.`	`0.25`	`You can target 1 Dragon monster you control; it gains ATK/DEF equal to the total Link Rating of the Link Monsters currently on the field x 100, until the end of the opponent's turn. You can only use this effect of "Guardragon Shield" once per turn. Once per turn, if exactly 1 Dragon monster you control would be destroyed by battle or card effect, you can send 1 Normal Monster from your hand or Deck to the GY instead.`

Loss: CoSENTLoss with these parameters:

{
    "scale": 20.0,
    "similarity_fct": "pairwise_cos_sim"
}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 16
per_device_eval_batch_size: 16
num_train_epochs: 5
warmup_ratio: 0.1
fp16: 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: 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: 1.0
num_train_epochs: 5
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: True
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
0.1786	100	3.8917	3.7898
0.3571	200	3.7289	3.7576
0.5357	300	3.6719	3.7211
0.7143	400	3.6294	3.6751
0.8929	500	3.5188	3.6291
1.0714	600	3.6794	3.5768
1.25	700	3.4962	3.5798
1.4286	800	3.4325	3.6149
1.6071	900	3.3956	3.6151
1.7857	1000	3.2907	3.7533
1.9643	1100	3.3685	3.5106
2.1429	1200	3.502	3.4844
2.3214	1300	3.3796	3.6363
2.5	1400	3.2383	3.5744
2.6786	1500	3.1346	3.6568
2.8571	1600	3.1808	3.6278
3.0357	1700	3.3241	3.4786
3.2143	1800	3.2864	3.4705
3.3929	1900	3.2056	3.5290
3.5714	2000	3.1519	3.6228
3.75	2100	3.0889	3.5919
3.9286	2200	2.9385	3.6148
4.1071	2300	3.2051	3.5180
4.2857	2400	3.2581	3.5216
4.4643	2500	3.0765	3.5968
4.6429	2600	2.9497	3.6496
4.8214	2700	2.8502	3.6804
5.0	2800	3.1919	3.6668

Framework Versions

Python: 3.10.12
Sentence Transformers: 3.3.1
Transformers: 4.47.1
PyTorch: 2.5.1+cu121
Accelerate: 1.2.1
Datasets: 3.2.0
Tokenizers: 0.21.0

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",
}

CoSENTLoss

@online{kexuefm-8847,
    title={CoSENT: A more efficient sentence vector scheme than Sentence-BERT},
    author={Su Jianlin},
    year={2022},
    month={Jan},
    url={https://kexue.fm/archives/8847},
}

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Safetensors

Model size

4.39M params

Tensor type

F32

Model tree for Tien09/tiny_bert_ft_sim_score

Base model

prajjwal1/bert-tiny

Finetuned

(81)

this model

Dataset used to train Tien09/tiny_bert_ft_sim_score

Paper for Tien09/tiny_bert_ft_sim_score

Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks

Paper • 1908.10084 • Published Aug 27, 2019 • 11