SentenceTransformer based on TaylorAI/bge-micro-v2

This is a sentence-transformers model finetuned from TaylorAI/bge-micro-v2 on the mtg_cards-2025-04-04 dataset. 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: TaylorAI/bge-micro-v2
• Maximum Sequence Length: 512 tokens
• Output Dimensionality: 384 dimensions
• Similarity Function: Cosine Similarity
• Training Dataset:
  • mtg_cards-2025-04-04
• Language: en

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': 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})
)

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("philipp-zettl/bge-micro-v2-mtg")
# Run inference
sentences = [
    '141a031d-f899-497b-adf7-4af142078085_0367fac8-6990-4544-ac7d-ed363b55a9cf',
    "Title: Quirion Explorer\nCost: {1}{G}\nColors: ['G']\nType: Creature — Elf Druid Scout\nDesc: {T}: Add one mana of any color that a land an opponent controls could produce.",
    "Title: Savage Hunger\nCost: {2}{G}\nColors: ['G']\nType: Enchantment — Aura\nDesc: Enchant creature\nEnchanted creature gets +1/+0 and has trample.\nCycling {2} ({2}, Discard this card: Draw a card.)",
]
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

• Datasets: sts-dev and sts-test
• Evaluated with EmbeddingSimilarityEvaluator

Training Details

Training Dataset

mtg_cards-2025-04-04

• Dataset: mtg_cards-2025-04-04 at a35ccc4
• Size: 2,839,738 training samples
• Columns: uuid, sentence_1, sentence_2, image_1, image_2, and score
• Approximate statistics based on the first 1000 samples:

  	uuid	sentence_1	sentence_2	image_1	image_2	score
  type	string	string	string	string	string	float
  details	min: 49 tokensmean: 56.99 tokensmax: 65 tokens	min: 17 tokensmean: 69.4 tokensmax: 180 tokens	min: 15 tokensmean: 68.59 tokensmax: 166 tokens	min: 53 tokensmean: 58.17 tokensmax: 64 tokens	min: 52 tokensmean: 58.28 tokensmax: 64 tokens	min: -1.0mean: -0.43max: 0.5

• Samples:

  uuid	sentence_1	sentence_2	image_1	image_2	score
  08f9b863-10b7-46d6-badd-97381e6c7c5e_4330efa7-a11b-4776-9fb0-1cae8aed67b1	Title: Blast ZoneType: LandDesc: This land enters with a charge counter on it.{T}: Add {C}.{X}{X}, {T}: Put X charge counters on this land.{3}, {T}, Sacrifice this land: Destroy each nonland permanent with mana value equal to the number of charge counters on this land.	Title: Tom van de Logt Bio (2000)Type: CardDesc: Quarterfinalist Tom van de Logt posted a perfect 6—0 record during the Standard portion of this year's World Championships. The 19-year-old Groesbeek, Holland native was playing a deck that had a big impact on the metagame this year, "Replenish." This deck used cards like Attunement and Frantic Search to put powerful enchantments, such as Parallax Wave and Opalescence, into the graveyard and then used Replenish to put them all back into play at once.	https://cards.scryfall.io/normal/front/0/8/08f9b863-10b7-46d6-badd-97381e6c7c5e.jpg?1674423042	https://cards.scryfall.io/normal/front/4/3/4330efa7-a11b-4776-9fb0-1cae8aed67b1.jpg?1562767017	0.25
  abe9cf1e-d398-41e0-8b11-afe1015e4fd9_40cb67f7-b4e1-423b-8f55-d44ed383e778	Title: Coral NetCost: {U}Colors: ['U']Type: Enchantment — AuraDesc: Enchant green or white creatureEnchanted creature has "At the beginning of your upkeep, sacrifice this creature unless you discard a card."	Title: Silumgar ButcherCost: {4}{B}Colors: ['B']Type: Creature — Zombie DjinnDesc: Exploit (When this creature enters, you may sacrifice a creature.)When this creature exploits a creature, target creature gets -3/-3 until end of turn.	https://cards.scryfall.io/normal/front/a/b/abe9cf1e-d398-41e0-8b11-afe1015e4fd9.jpg?1562631469	https://cards.scryfall.io/normal/front/4/0/40cb67f7-b4e1-423b-8f55-d44ed383e778.jpg?1562785294	-1.0
  3dd13408-b4db-42e7-bf3c-d46716538a7c_05a6dc90-3997-4911-8bd6-854c85eca35b	Title: Rishadan BrigandCost: {4}{U}Colors: ['U']Type: Creature — Human PirateDesc: FlyingWhen this creature enters, each opponent sacrifices a permanent of their choice unless they pay {3}.This creature can block only creatures with flying.	Title: Banishing StrokeCost: {5}{W}Colors: ['W']Type: InstantDesc: Put target artifact, creature, or enchantment on the bottom of its owner's library.Miracle {W} (You may cast this card for its miracle cost when you draw it if it's the first card you drew this turn.)	https://cards.scryfall.io/normal/front/3/d/3dd13408-b4db-42e7-bf3c-d46716538a7c.jpg?1632145390	https://cards.scryfall.io/normal/front/0/5/05a6dc90-3997-4911-8bd6-854c85eca35b.jpg?1723433851	-1.0

• Loss: CosineSimilarityLoss with these parameters:

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

Evaluation Dataset

mtg_cards-2025-04-04

• Dataset: mtg_cards-2025-04-04 at a35ccc4
• Size: 74,730 evaluation samples
• Columns: uuid, sentence_1, sentence_2, image_1, image_2, and score
• Approximate statistics based on the first 1000 samples:

  	uuid	sentence_1	sentence_2	image_1	image_2	score
  type	string	string	string	string	string	float
  details	min: 50 tokensmean: 56.9 tokensmax: 65 tokens	min: 14 tokensmean: 68.44 tokensmax: 181 tokens	min: 15 tokensmean: 69.49 tokensmax: 179 tokens	min: 52 tokensmean: 58.22 tokensmax: 64 tokens	min: 52 tokensmean: 58.21 tokensmax: 64 tokens	min: -1.0mean: -0.44max: 0.75

• Samples:

  uuid	sentence_1	sentence_2	image_1	image_2	score
  6bdd8645-aee9-44cb-acaa-2674f55cdf2f_b34bb149-2e50-462e-8b83-5c8339bb3aff	Title: Syr Cadian, Knight OwlCost: {3}{W}{W}Colors: ['W']Type: Legendary Creature — Bird KnightDesc: Knightlifelink (Damage dealt by Knights you control also causes you to gain that much life.){W}: Syr Cadian gains vigilance until end of turn. Activate only from sunrise to sunset.{B}: Syr Cadian gains flying until end of turn. Activate only from sunset to sunrise.	Title: Non-Human CannonballCost: {2}{R}Colors: ['R']Type: Artifact Creature — Clown RobotDesc: When this creature dies, roll a six-sided die. If the result is 4 or less, this creature deals that much damage to you.	https://cards.scryfall.io/normal/front/6/b/6bdd8645-aee9-44cb-acaa-2674f55cdf2f.jpg?1664317187	https://cards.scryfall.io/normal/front/b/3/b34bb149-2e50-462e-8b83-5c8339bb3aff.jpg?1673917877	0.25
  860f4304-38f1-4c2f-a122-2590619522fd_08d6db9b-b2da-4148-aa49-8c2fecac6e32	Title: Hindering LightCost: {W}{U}Colors: ['U', 'W']Type: InstantDesc: Counter target spell that targets you or a permanent you control.Draw a card.	Title: Gleam of ResistanceCost: {4}{W}Colors: ['W']Type: InstantDesc: Creatures you control get +1/+2 until end of turn. Untap those creatures.Basic landcycling {1}{W} ({1}{W}, Discard this card: Search your library for a basic land card, reveal it, put it into your hand, then shuffle.)	https://cards.scryfall.io/normal/front/8/6/860f4304-38f1-4c2f-a122-2590619522fd.jpg?1712353583	https://cards.scryfall.io/normal/front/0/8/08d6db9b-b2da-4148-aa49-8c2fecac6e32.jpg?1573505575	0.25
  91b448f4-aa0c-42c7-a771-e8dd20e0520c_46f810c2-310e-42f5-ab1f-d56396cf5124	Title: Practiced TacticsCost: {W}Colors: ['W']Type: InstantDesc: Choose target attacking or blocking creature. Practiced Tactics deals damage to that creature equal to twice the number of creatures in your party. (Your party consists of up to one each of Cleric, Rogue, Warrior, and Wizard.)	Title: Anointer PriestCost: {1}{W}Colors: ['W']Type: Creature — Human ClericDesc: Whenever a creature token you control enters, you gain 1 life.Embalm {3}{W} ({3}{W}, Exile this card from your graveyard: Create a token that's a copy of it, except it's a white Zombie Human Cleric with no mana cost. Embalm only as a sorcery.)	https://cards.scryfall.io/normal/front/9/1/91b448f4-aa0c-42c7-a771-e8dd20e0520c.jpg?1604192922	https://cards.scryfall.io/normal/front/4/6/46f810c2-310e-42f5-ab1f-d56396cf5124.jpg?1599769231	0.25

• Loss: CosineSimilarityLoss with these parameters:

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

Training Hyperparameters

Non-Default Hyperparameters

• eval_strategy: steps
• per_device_train_batch_size: 64
• per_device_eval_batch_size: 64
• learning_rate: 2e-05
• num_train_epochs: 1
• warmup_ratio: 0.1
• log_level_replica: passive
• log_on_each_node: False
• logging_nan_inf_filter: False
• push_to_hub: True
• resume_from_checkpoint: ./models/bge-micro-v2-mtg/
• hub_model_id: philipp-zettl/bge-micro-v2-mtg
• hub_always_push: True
• batch_sampler: no_duplicates

All Hyperparameters

• overwrite_output_dir: False
• do_predict: False
• eval_strategy: steps
• prediction_loss_only: True
• per_device_train_batch_size: 64
• 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
• torch_empty_cache_steps: None
• learning_rate: 2e-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: passive
• log_on_each_node: False
• logging_nan_inf_filter: False
• 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}
• tp_size: 0
• 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: True
• resume_from_checkpoint: ./models/bge-micro-v2-mtg/
• hub_model_id: philipp-zettl/bge-micro-v2-mtg
• hub_strategy: every_save
• hub_private_repo: None
• hub_always_push: True
• 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

Framework Versions

• Python: 3.10.14
• Sentence Transformers: 4.0.2
• Transformers: 4.50.3
• PyTorch: 2.6.0+cu124
• Accelerate: 1.6.0
• Datasets: 3.5.0
• Tokenizers: 0.21.1

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",
}