This is a sentence-transformers model finetuned from Mihaiii/gte-micro-v4 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: Mihaiii/gte-micro-v4
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.

python

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("philipp-zettl/gte-micro-v4-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

Metric	sts-dev	sts-test
pearson_cosine	0.5888	0.586
spearman_cosine	0.6572	0.6549

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 tokens mean: 56.99 tokens max: 65 tokens	min: 17 tokens mean: 69.4 tokens max: 180 tokens	min: 15 tokens mean: 68.59 tokens max: 166 tokens	min: 53 tokens mean: 58.17 tokens max: 64 tokens	min: 52 tokens mean: 58.28 tokens max: 64 tokens	min: -1.0 mean: -0.43 max: 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 Zone[object Object]Type: Land[object Object]Desc: This land enters with a charge counter on it.[object Object]{T}: Add {C}.[object Object]{X}{X}, {T}: Put X charge counters on this land.[object Object]{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)[object Object]Type: Card[object Object]Desc: 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.	`[object Object]`	`[object Object]`	`0.25`
`abe9cf1e-d398-41e0-8b11-afe1015e4fd9_40cb67f7-b4e1-423b-8f55-d44ed383e778`	`Title: Coral Net[object Object]Cost: {U}[object Object]Colors: ['U'][object Object]Type: Enchantment — Aura[object Object]Desc: Enchant green or white creature[object Object]Enchanted creature has "At the beginning of your upkeep, sacrifice this creature unless you discard a card."`	`Title: Silumgar Butcher[object Object]Cost: {4}{B}[object Object]Colors: ['B'][object Object]Type: Creature — Zombie Djinn[object Object]Desc: Exploit (When this creature enters, you may sacrifice a creature.)[object Object]When this creature exploits a creature, target creature gets -3/-3 until end of turn.`	`[object Object]`	`[object Object]`	`-1.0`
`3dd13408-b4db-42e7-bf3c-d46716538a7c_05a6dc90-3997-4911-8bd6-854c85eca35b`	`Title: Rishadan Brigand[object Object]Cost: {4}{U}[object Object]Colors: ['U'][object Object]Type: Creature — Human Pirate[object Object]Desc: Flying[object Object]When this creature enters, each opponent sacrifices a permanent of their choice unless they pay {3}.[object Object]This creature can block only creatures with flying.`	`Title: Banishing Stroke[object Object]Cost: {5}{W}[object Object]Colors: ['W'][object Object]Type: Instant[object Object]Desc: Put target artifact, creature, or enchantment on the bottom of its owner's library.[object Object]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.)`	`[object Object]`	`[object Object]`	`-1.0`

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

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 tokens mean: 56.9 tokens max: 65 tokens	min: 14 tokens mean: 68.44 tokens max: 181 tokens	min: 15 tokens mean: 69.49 tokens max: 179 tokens	min: 52 tokens mean: 58.22 tokens max: 64 tokens	min: 52 tokens mean: 58.21 tokens max: 64 tokens	min: -1.0 mean: -0.44 max: 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 Owl[object Object]Cost: {3}{W}{W}[object Object]Colors: ['W'][object Object]Type: Legendary Creature — Bird Knight[object Object]Desc: Knightlifelink (Damage dealt by Knights you control also causes you to gain that much life.)[object Object]{W}: Syr Cadian gains vigilance until end of turn. Activate only from sunrise to sunset.[object Object]{B}: Syr Cadian gains flying until end of turn. Activate only from sunset to sunrise.`	`Title: Non-Human Cannonball[object Object]Cost: {2}{R}[object Object]Colors: ['R'][object Object]Type: Artifact Creature — Clown Robot[object Object]Desc: When this creature dies, roll a six-sided die. If the result is 4 or less, this creature deals that much damage to you.`	`[object Object]`	`[object Object]`	`0.25`
`860f4304-38f1-4c2f-a122-2590619522fd_08d6db9b-b2da-4148-aa49-8c2fecac6e32`	`Title: Hindering Light[object Object]Cost: {W}{U}[object Object]Colors: ['U', 'W'][object Object]Type: Instant[object Object]Desc: Counter target spell that targets you or a permanent you control.[object Object]Draw a card.`	`Title: Gleam of Resistance[object Object]Cost: {4}{W}[object Object]Colors: ['W'][object Object]Type: Instant[object Object]Desc: Creatures you control get +1/+2 until end of turn. Untap those creatures.[object Object]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.)`	`[object Object]`	`[object Object]`	`0.25`
`91b448f4-aa0c-42c7-a771-e8dd20e0520c_46f810c2-310e-42f5-ab1f-d56396cf5124`	`Title: Practiced Tactics[object Object]Cost: {W}[object Object]Colors: ['W'][object Object]Type: Instant[object Object]Desc: 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 Priest[object Object]Cost: {1}{W}[object Object]Colors: ['W'][object Object]Type: Creature — Human Cleric[object Object]Desc: Whenever a creature token you control enters, you gain 1 life.[object Object]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.)`	`[object Object]`	`[object Object]`	`0.25`

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: 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/gte-micro-v4-mtg/
hub_model_id: philipp-zettl/gte-micro-v4-mtg
hub_always_push: 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: 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/gte-micro-v4-mtg/
hub_model_id: philipp-zettl/gte-micro-v4-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

Epoch	Step	Training Loss	Validation Loss	sts-dev_spearman_cosine	sts-test_spearman_cosine
-1	-1	-	-	0.3315	-
0.0113	500	1.4254	-	-	-
0.0225	1000	0.3809	-	-	-
0.0338	1500	0.3494	-	-	-
0.0451	2000	0.3481	-	-	-
0.0563	2500	0.3466	-	-	-
0.0676	3000	0.3475	-	-	-
0.0789	3500	0.3467	-	-	-
0.0901	4000	0.3467	-	-	-
0.1014	4500	0.348	-	-	-
0.1127	5000	0.3469	0.3448	0.6769	-
0.1240	5500	0.3493	-	-	-
0.1352	6000	0.3463	-	-	-
0.1465	6500	0.3457	-	-	-
0.1578	7000	0.3449	-	-	-
0.1690	7500	0.3432	-	-	-
0.1803	8000	0.3424	-	-	-
0.1916	8500	0.3443	-	-	-
0.2028	9000	0.344	-	-	-
0.2141	9500	0.3466	-	-	-
0.2254	10000	0.3421	0.3449	0.6726	-
0.2366	10500	0.3422	-	-	-
0.2479	11000	0.3439	-	-	-
0.2592	11500	0.3454	-	-	-
0.2704	12000	0.3476	-	-	-
0.2817	12500	0.3461	-	-	-
0.2930	13000	0.3483	-	-	-
0.3043	13500	0.344	-	-	-
0.3155	14000	0.3496	-	-	-
0.3268	14500	0.3448	-	-	-
0.3381	15000	0.3462	0.3442	0.6632	-
0.3493	15500	0.3446	-	-	-
0.3606	16000	0.3443	-	-	-
0.3719	16500	0.3444	-	-	-
0.3831	17000	0.3452	-	-	-
0.3944	17500	0.3467	-	-	-
0.4057	18000	0.3439	-	-	-
0.4169	18500	0.3437	-	-	-
0.4282	19000	0.3426	-	-	-
0.4395	19500	0.3435	-	-	-
0.4507	20000	0.3453	0.3443	0.6550	-
0.4620	20500	0.3439	-	-	-
0.4733	21000	0.3434	-	-	-
0.4846	21500	0.3477	-	-	-
0.4958	22000	0.3471	-	-	-
0.5071	22500	0.3468	-	-	-
0.5184	23000	0.3453	-	-	-
0.5296	23500	0.3447	-	-	-
0.5409	24000	0.3441	-	-	-
0.5522	24500	0.3459	-	-	-
0.5634	25000	0.3431	0.3447	0.6558	-
0.5747	25500	0.3435	-	-	-
0.5860	26000	0.3464	-	-	-
0.5972	26500	0.3436	-	-	-
0.6085	27000	0.3446	-	-	-
0.6198	27500	0.3401	-	-	-
0.6310	28000	0.347	-	-	-
0.6423	28500	0.3412	-	-	-
0.6536	29000	0.3427	-	-	-
0.6648	29500	0.3423	-	-	-
0.6761	30000	0.3407	0.3418	0.6612	-
0.6874	30500	0.3404	-	-	-
0.6987	31000	0.3413	-	-	-
0.7099	31500	0.3434	-	-	-
0.7212	32000	0.3437	-	-	-
0.7325	32500	0.3442	-	-	-
0.7437	33000	0.3413	-	-	-
0.7550	33500	0.3441	-	-	-
0.7663	34000	0.3387	-	-	-
0.7775	34500	0.3416	-	-	-
0.7888	35000	0.3409	0.3392	0.6554	-
0.8001	35500	0.3414	-	-	-
0.8113	36000	0.338	-	-	-
0.8226	36500	0.3385	-	-	-
0.8339	37000	0.3391	-	-	-
0.8451	37500	0.3381	-	-	-
0.8564	38000	0.3372	-	-	-
0.8677	38500	0.3391	-	-	-
0.8790	39000	0.3404	-	-	-
0.8902	39500	0.3399	-	-	-
0.9015	40000	0.3413	0.3376	0.6572	-
0.9128	40500	0.3408	-	-	-
0.9240	41000	0.342	-	-	-
0.9353	41500	0.3389	-	-	-
0.9466	42000	0.3375	-	-	-
0.9578	42500	0.3378	-	-	-
0.9691	43000	0.3386	-	-	-
0.9804	43500	0.3377	-	-	-
0.9916	44000	0.3362	-	-	-
-1	-1	-	-	-	0.6549

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

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",
}