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rolloraq/frantic_ruleriot

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CrossEncoder based on cross-encoder/ms-marco-MiniLM-L6-v2

This is a Cross Encoder model finetuned from cross-encoder/ms-marco-MiniLM-L6-v2 using the sentence-transformers library. It computes scores for pairs of texts, which can be used for text reranking and semantic search.

Model Details

Model Description

  • —Model Type: Cross Encoder
  • —Base model: cross-encoder/ms-marco-MiniLM-L6-v2 <!-- at revision ce0834f22110de6d9222af7a7a03628121708969 -->
  • —Maximum Sequence Length: 256 tokens
  • —Number of Output Labels: 1 label <!-- - Training Dataset: Unknown --> <!-- - Language: Unknown --> <!-- - License: Unknown -->

Model Sources

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 CrossEncoder

# Download from the 🤗 Hub
model = CrossEncoder("cross_encoder_model_id")
# Get scores for pairs of texts
pairs = [
    ['Was bewirkt GIFT?', 'Schenke jemandem zwei deiner Handkarten. Hast du nur eine, verschenke nur diese.'],
    ['Wie viele Karten hat das Basisspiel?', 'FRANTIC GRUNDSPIEL\n125 Spielkarten\n(schwarze Rückseite)\n81 Zahlenkarten\n· 18 blaue Karten (1 bis 9)\n· 18 rote Karten (1 bis 9)\n· 18 grüne Karten (1 bis 9)\n· 18 gelbe Karten (1 bis 9)\n· 9 schwarze Karten (1 bis 9)\n44 Spezialkarten\n· 8 «Gift» Karten (2 pro Farbe)\n· 4 «Exchange» Karten\n(1 pro Farbe)\n· 4 «2nd Chance» Karten\n(1 pro Farbe)\n· 4 «Skip» Karten (1 pro Farbe)\n· 11 «Fantastic» Karten\n· 5 «Fantastic Four» Karten\n· 4 «Counterattack» Karten\n· 2 «Equality» Karten\n· 1 «Nice Try» Karte\n· 1 «Fuck You» Karte\n+ 20 einzigartige Ereigniskarten\n(weisse Rückseite)'],
    ['Was macht DOOMSDAY?', 'DOOMSDAY\nDie Runde ist sofort beendet. Alle er\nhalten 50 Punkte. Die Punkte eurer Handkarten werden nicht gewertet.'],
    ['Was ist NICE TRY?', 'Das Reinwerfen von Nice Try zählt nicht\nals Spielzug. Wünsch dir trotzdem eine\nFarbe.\nHinweis: Haben mehrere Spieler*innen\nihre letzte Handkarte abgelegt, müssen\nalle von ihnen 3 Karten ziehen.\nIn deinem Spielzug:'],
    ['Was bewirkt THE END?', 'Diese Karte zählt am Ende einer Runde\n7 Punkte. THE END S'],
]
scores = model.predict(pairs)
print(scores.shape)
# (5,)

# Or rank different texts based on similarity to a single text
ranks = model.rank(
    'Was bewirkt GIFT?',
    [
        'Schenke jemandem zwei deiner Handkarten. Hast du nur eine, verschenke nur diese.',
        'FRANTIC GRUNDSPIEL\n125 Spielkarten\n(schwarze Rückseite)\n81 Zahlenkarten\n· 18 blaue Karten (1 bis 9)\n· 18 rote Karten (1 bis 9)\n· 18 grüne Karten (1 bis 9)\n· 18 gelbe Karten (1 bis 9)\n· 9 schwarze Karten (1 bis 9)\n44 Spezialkarten\n· 8 «Gift» Karten (2 pro Farbe)\n· 4 «Exchange» Karten\n(1 pro Farbe)\n· 4 «2nd Chance» Karten\n(1 pro Farbe)\n· 4 «Skip» Karten (1 pro Farbe)\n· 11 «Fantastic» Karten\n· 5 «Fantastic Four» Karten\n· 4 «Counterattack» Karten\n· 2 «Equality» Karten\n· 1 «Nice Try» Karte\n· 1 «Fuck You» Karte\n+ 20 einzigartige Ereigniskarten\n(weisse Rückseite)',
        'DOOMSDAY\nDie Runde ist sofort beendet. Alle er\nhalten 50 Punkte. Die Punkte eurer Handkarten werden nicht gewertet.',
        'Das Reinwerfen von Nice Try zählt nicht\nals Spielzug. Wünsch dir trotzdem eine\nFarbe.\nHinweis: Haben mehrere Spieler*innen\nihre letzte Handkarte abgelegt, müssen\nalle von ihnen 3 Karten ziehen.\nIn deinem Spielzug:',
        'Diese Karte zählt am Ende einer Runde\n7 Punkte. THE END S',
    ]
)
# [{'corpus_id': ..., 'score': ...}, {'corpus_id': ..., 'score': ...}, ...]

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Downstream Usage (Sentence Transformers)

You can finetune this model on your own dataset.

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

</details> -->

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Training Details

Training Dataset

Unnamed Dataset
  • —Size: 38 training samples
  • —Columns: <code>sentence0</code>, <code>sentence1</code>, and <code>label</code>
  • —Approximate statistics based on the first 38 samples: | | sentence0 | sentence1 | label | |:--------|:-----------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------|:---------------------------------------------------------------| | type | string | string | float | | details | <ul><li>min: 17 characters</li><li>mean: 22.95 characters</li><li>max: 36 characters</li></ul> | <ul><li>min: 30 characters</li><li>mean: 187.87 characters</li><li>max: 562 characters</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.34</li><li>max: 1.0</li></ul> |
  • —Samples: | sentence0 | sentence1 | label | |:--------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------| | <code>Was bewirkt GIFT?</code> | <code>Schenke jemandem zwei deiner Handkarten. Hast du nur eine, verschenke nur diese.</code> | <code>1.0</code> | | <code>Wie viele Karten hat das Basisspiel?</code> | <code>FRANTIC GRUNDSPIEL<br>125 Spielkarten<br>(schwarze Rückseite)<br>81 Zahlenkarten<br>· 18 blaue Karten (1 bis 9)<br>· 18 rote Karten (1 bis 9)<br>· 18 grüne Karten (1 bis 9)<br>· 18 gelbe Karten (1 bis 9)<br>· 9 schwarze Karten (1 bis 9)<br>44 Spezialkarten<br>· 8 «Gift» Karten (2 pro Farbe)<br>· 4 «Exchange» Karten<br>(1 pro Farbe)<br>· 4 «2nd Chance» Karten<br>(1 pro Farbe)<br>· 4 «Skip» Karten (1 pro Farbe)<br>· 11 «Fantastic» Karten<br>· 5 «Fantastic Four» Karten<br>· 4 «Counterattack» Karten<br>· 2 «Equality» Karten<br>· 1 «Nice Try» Karte<br>· 1 «Fuck You» Karte<br>+ 20 einzigartige Ereigniskarten<br>(weisse Rückseite)</code> | <code>0.0</code> | | <code>Was macht DOOMSDAY?</code> | <code>DOOMSDAY<br>Die Runde ist sofort beendet. Alle er<br>halten 50 Punkte. Die Punkte eurer Handkarten werden nicht gewertet.</code> | <code>0.0</code> |
  • —Loss: <code>BinaryCrossEntropyLoss</code> with these parameters:
json
  {
      "activation_fn": "torch.nn.modules.linear.Identity",
      "pos_weight": null
  }

Training Hyperparameters

Non-Default Hyperparameters
  • —per_device_train_batch_size: 4
  • —per_device_eval_batch_size: 4
  • —num_train_epochs: 8
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: 4
  • —per_device_eval_batch_size: 4
  • —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: 8
  • —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
  • —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
  • —eval_use_gather_object: False
  • —average_tokens_across_devices: False
  • —prompts: None
  • —batch_sampler: batch_sampler
  • —multi_dataset_batch_sampler: proportional

</details>

Framework Versions

  • —Python: 3.12.1
  • —Sentence Transformers: 4.1.0
  • —Transformers: 4.52.3
  • —PyTorch: 2.6.0+cpu
  • —Accelerate: 1.7.0
  • —Datasets: 3.6.0
  • —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",
}

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