Modelpublic

KiruruP/anime-recommendation-multilingual-mpnet-base-v2

sourceHugging Faceapache-2.0updated 1y agoView on Hugging Face

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Model Card

Anime Recommendation based on User query

This is a sentence-transformers model finetuned from sentence-transformers/paraphrase-multilingual-mpnet-base-v2. It maps sentences & paragraphs to a 768-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/paraphrase-multilingual-mpnet-base-v2
—Maximum Sequence Length: 128 tokens
—Output Dimensionality: 768 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': 128, 'do_lower_case': False}) with Transformer model: XLMRobertaModel 
  (1): Pooling({'word_embedding_dimension': 768, '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:

bash

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("KiruruP/anime-recommendation-multilingual-mpnet-base-v2")
# Run inference
sentences = [
    'What is the story about a man named Rock Okajima who joins a pirate mercenary group in Roanapur, Thailand and must decide between staying in the criminal underworld or trying to escape?',
    'Rokurou "Rock" Okajima has joined the Lagoon Company, a pirate mercenary group which operates out of Roanapur, Thailand. Despite his initial protests, this filthy slum of depraved souls and merciless criminals now serves as the former salaryman\'s home. Stranded, with nothing left of his past life but the clothes on his back and his inner morality, Rock is forced to perform jobs alongside the other members of the Lagoon crew. Berated for his lack of spine as he wades through the underbelly of society, he must decide whether to continue on amidst the gunfire and ruthlessness or risk everything he has in an attempt to be free. Whether he chooses the comfort of a familiar land or the freedom of being an outlaw, his decision will have lasting consequences on the crew who gave him a home.',
    "Comical action adventure film set in the future world, like a dystopian science fiction. Shinnosuke's future fiancee, Tamiko Kaneari comes from the future via the time machine. She says her father Masuzo Kaneari captures adult Shinnosuke in the future world and they needs the power of Shinnosuke at age 5 to rescue him. She takes Shinnosuke and Kasukabe Defense Forces members to the future big city Neotokio, which is the world ruled by Masuzo Kaneari, the president of the electric power company Kaneari Electric.",
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

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

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Training Details

Training Dataset

Unnamed Dataset

—MAL Anime Top 2500 (Sorted by Score)
—Size: 4,000 training samples
—Columns: <code>sentence0</code>, <code>sentence1</code>, and <code>label</code>
—Approximate statistics based on the first 1000 samples: | | sentence0 | sentence1 | label | |:--------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:--------------------------------------------------------------| | type | string | string | float | | details | <ul><li>min: 18 tokens</li><li>mean: 47.9 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>min: 51 tokens</li><li>mean: 122.5 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>min: 1.0</li><li>mean: 1.0</li><li>max: 1.0</li></ul> |
—Samples: | sentence0 | sentence1 | label | |:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------| | <code>What is the query for a story about a teenager seeking revenge on a villainous scientist with an army of mechanical beasts, located on an island in the Aegean Sea, who controls them with a cane, and has a loyal henchman? The teenager pilots a powerful robot made of indestructible metal to stop them.</code> | <code>The villainous Dr. Hell has amassed an army of mechanical beasts in his secret hideaway, the island of Bardos located in the Aegean Sea. He is capable of controlling mechanized beasts with his cane, and instructs them to unleash devastating attacks. However, Dr. Hell doesn't do all the dirty work by himself; he has his loyal henchman Baron Ashura to carry out his devilish plans. There are also those that will see to it that evil does not prevail. Kouji Kabuto is the young and feisty teenager with a score to settle: his goal is avenging the murder of his grandfather by Dr. Hell. And he might just be able to pull it off, as he is the pilot of Mazinger Z, a mighty giant robot made out of an indestructible metal known as Super-Alloy Z. Mazinger Z boasts several powerful special attacks. By channeling Photonic Energy through its eyes, and unleashing the Koushiryoku Beam, it can cause great destruction. But things get really cool when Mazinger Z launches its Rocket Punch attack. Dr. Hell and...</code> | <code>1.0</code> | | <code>What is a manga about a struggling artist named Da Shu who finds companionship with a young man with cat ears?</code> | <code>Da Shu is a a manga creator who grew up in an orphanage, and whose works do not sell very well. Da Shu lives each lonely day in boredom, but one day he meets a young man named Miao who has cat ears, and his everyday life completely changes. Miao gets in trouble every day, but for the first time in his life, Da Shu's heart experiences "warmth."</code> | <code>1.0</code> | | <code>What is a group of amnesiac strangers forced to become soldiers in a dangerous world called, where they must form a party to survive?</code> | <code>Fear, survival, instinct. Thrown into a foreign land with nothing but hazy memories and the knowledge of their name, they can feel only these three emotions resonating deep within their souls. A group of strangers is given no other choice than to accept the only paying job in this game-like world—the role of a soldier in the Reserve Army—and eliminate anything that threatens the peace in their new world, Grimgar. When all of the stronger candidates join together, those left behind must create a party together to survive: Manato, a charismatic leader and priest; Haruhiro, a nervous thief; Yume, a cheerful hunter; Shihoru, a shy mage; Moguzo, a kind warrior; and Ranta, a rowdy dark knight. Despite its resemblance to one, this is no game—there are no redos or respawns; it is kill or be killed. It is now up to this ragtag group of unlikely fighters to survive together in a world where life and death are separated only by a fine line.</code> | <code>1.0</code> |
—Loss: <code>CosineSimilarityLoss</code> with these parameters:

json

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

Training Hyperparameters

Non-Default Hyperparameters

—per_device_train_batch_size: 16
—per_device_eval_batch_size: 16
—num_train_epochs: 10
—multi_dataset_batch_sampler: round_robin

All Hyperparameters

<details><summary>Click to expand</summary>

—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: 10
—max_steps: -1
—lr_scheduler_type: linear
—lr_scheduler_kwargs: {}
—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
—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: {'minnumparams': 0, 'xla': False, 'xlafsdpv2': False, 'xlafsdpgrad_ckpt': False}
—fsdp_transformer_layer_cls_to_wrap: None
—accelerator_config: {'splitbatches': False, 'dispatchbatches': None, 'evenbatches': True, 'useseedablesampler': True, 'nonblocking': False, 'gradientaccumulationkwargs': 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
—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: False
—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: False
—prompts: None
—batch_sampler: batch_sampler
—multi_dataset_batch_sampler: round_robin

</details>

Training Logs

Epoch	Step	Training Loss
2.0	500	0.044
4.0	1000	0.0109
6.0	1500	0.0042
8.0	2000	0.0024
10.0	2500	0.0017

Framework Versions

—Python: 3.11.13
—Sentence Transformers: 4.1.0
—Transformers: 4.54.0
—PyTorch: 2.6.0+cu124
—Accelerate: 1.9.0
—Datasets: 4.0.0
—Tokenizers: 0.21.2

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",
}

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