arcanum-quests-retriever

SentenceTransformer based on BAAI/bge-m3

This is a sentence-transformers model finetuned from BAAI/bge-m3. It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for retrieval.

Model Details

Model Description

• Model Type: Sentence Transformer
• Base model: BAAI/bge-m3 <!-- at revision 5617a9f61b028005a4858fdac845db406aefb181 -->
• Maximum Sequence Length: 8192 tokens
• Output Dimensionality: 1024 dimensions
• Similarity Function: Cosine Similarity
• Supported Modality: Text

<!-- - **Training Dataset:** Unknown --> <!-- - **Language:** Unknown --> <!-- - **License:** Unknown -->

Model Sources

• Documentation: Sentence Transformers Documentation
• Repository: Sentence Transformers on GitHub
• Hugging Face: Sentence Transformers on Hugging Face

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'transformer_task': 'feature-extraction', 'modality_config': {'text': {'method': 'forward', 'method_output_name': 'last_hidden_state'}}, 'module_output_name': 'token_embeddings', 'architecture': 'XLMRobertaModel'})
  (1): Pooling({'embedding_dimension': 1024, 'pooling_mode': 'cls', 'include_prompt': True})
  (2): Normalize({})
)

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("pameydorke/arcanum-quests-retriever")
# Run inference
sentences = [
    'steps to rescue prisoner',
    "Rescue Tollo Underhill. If the party finds and rescues Tollo randomly (without being assigned by K'an Hua) then the rewards are still the same The Pits has some interesting loot, so exploring it in its entirety may be a consideration",
    "Clean Up the Boil. Damian Maug's Path. The Closed Sewers (optional) If the player does not want to conduct a direct frontal assault on Damian Maug, then they may try a stealth approach The Bentley (see below) and the Port of Tarant have access to the closed sewers of Tarant Point 1 is the Port entrance and Point 3 is the access to the Bentley This route can be used to avoid the bulk of Maug's defenses The Bentley The player can find Maug (point 1) in his house on the west side of The Boil If the party goes for a frontal assault then they will have to slowly work their way through a maze of rooms with about a dozen powerful and well armed gang members scattered a few at a time Defeating them and getting to the final room will let the party finish off Maug and his bodyguard",
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.3610, 0.2773],
#         [0.3610, 1.0000, 0.1856],
#         [0.2773, 0.1856, 1.0000]])

<!-- ### Direct Usage (Transformers) <details><summary>Click to see the direct usage in Transformers</summary> </details> --> <!-- ### Downstream Usage (Sentence Transformers) You can finetune this model on your own dataset. <details><summary>Click to expand</summary> </details> --> <!-- ### Out-of-Scope Use *List how the model may foreseeably be misused and address what users ought not to do with the model.* -->

Evaluation

Metrics

Information Retrieval

• Dataset: arcanum-test
• Evaluated with <code>InformationRetrievalEvaluator</code>

<!-- ## Bias, Risks and Limitations *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* --> <!-- ### Recommendations *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* -->

Training Details

Training Dataset

Unnamed Dataset

• Size: 912 training samples
• Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
• Approximate statistics based on the first 912 samples:

  	anchor	positive	negative
  type	string	string	string
  details	<ul><li>min: 5 tokens</li><li>mean: 9.62 tokens</li><li>max: 19 tokens</li></ul>	<ul><li>min: 15 tokens</li><li>mean: 180.92 tokens</li><li>max: 1044 tokens</li></ul>	<ul><li>min: 15 tokens</li><li>mean: 237.09 tokens</li><li>max: 1044 tokens</li></ul>

• Samples:

  anchor	positive	negative
  <code>kill king caladon guide</code>	<code>Kill the King of Caladon. After talking with Mr Willoughsby, a man, Heinrich Jenks appears in the room outside. He wants you to assassinate the King Of Caladon, he tells you to meet Vernon in the Kitchen for more details. When the job is done he will meet you again in Grant’s Tavern and give you 20,000 gold.You can tell Mr Willoughsby and he will send Lorham, his bodyguard to deal with him (this botches the quest).Travelling to Caladon, you need to talk with Renard first and then you will find Vernon who gives you a key.You can also get into the palace by hiding in a crate on the Caladon waterfront.Upstairs you will find a guard patrolling, he will try and kill you on sight (getting past him depends on your style prowling and backstab work well here). Make sure you loot the rooms as there is a lot of jewellery. Go through the ventilation system and explore.It is worthwhile making your way to the treasure vault. The door can be difficult to pick (or break down without damaging weapons)....</code>	<code>Find Dudley Crosston. This quest is found in the "rumor" tab of the player's logbook, rather than in the "quest" tab If the player has expert training in Bow when he talks to Kietzal in Black Root, then this quest can be done concurrently with the Bow Mastery Quest Otherwise the Mastery Quest cannot be started until reaching Caladon, and talking to Kietzal a second time And since access to Caladon is only available later in the game, it can be advantageous to get expert training in Bow BEFORE taking the Dudley quest if Bow Mastery is desired, or simply if the player wishes to get the Bow of Ecclesiastes early</code>
  <code>gambliing quest walkthrough</code>	<code>Skill mastery quest. Gambling mastery. Gamble with Gurin Rockharrow. Women are refused entry to the Gentlemen's Club, but they still have a few avenues: Pick Pocket the key from the doorman Use Unlocking Cantrip to unbar the door Find out the name of the Club Owner from the doorman, getting his address from the Hall of Records, and then paying him a visit Once you meet with Wendell Wellington the options branch further: Pay a bribe and sleep with him for a special invitation Pick Pocket the invite Kill him and his bodyguard and loot his corpse for the invite With 8 points (2 ranks) of melee, you can threaten him for it With 4 points (1 rank) of persuasion and a Reaction Modifier of 81 you can sweet talk him for it Using this invitation will allow a lady to come and go as she pleases</code>	<code>Skill mastery quest. Persuasion mastery. Negotiations with Caladon. Willoughsby will comment about Orator of Ashbury reputation from the quest Monument Planning. Willoughsby will remain in Caladon for the rest of the game. The City Hall dialogue will be inaccessible, making it impossible for Sebastian to appear in The Boil. If the copy of the Agreement is somehow lost, the living one can still report its content to Willoughsby. After the negotiations are completed, independently of the result, King Farad will be "Leaving immediately for Tarant" and disappear; furthermore, the quest will be botched if the King is killed. Making it mutually exclusive with Assassinate King Farad of Caladon. There is a cut outcome left in the game code. If Tarant's counter was lower or equal to 1, currently an impossible result, Willoughsby would have been furious and wouldn't have given a reward,</code>
  <code>where is the key for big warehouse</code>	<code>Rid Mr. Plough's Warehouses of Rats. Talk to Simon Plough at the warehouse entrance and ask about his problem. Visit the smaller warehouse to the left first and kill the rats inside. You will also find a key in one of the barrels. Use the key to enter the bigger warehouse and kill everything inside. Return to Simon to finish the quest.</code>	<code>Caladon Thieves Underground. Adam Maxwell. Steal the book "Divinations and Magicks For The Adept Wizard" The book is found in a locked case in the library of the Misk Residence. The front room is guarded by the butler at night, and also by Mrs. Misk by day. Steal the Necklace of Queen Kna Sea The necklace is found in a locked safe in the Caladon Museum. The museum has no other visitors, but it is guarded by several mechanical arachnids.</code>

• Loss: <code>MultipleNegativesRankingLoss</code> with these parameters:

  {
      "scale": 20.0,
      "similarity_fct": "cos_sim",
      "gather_across_devices": false,
      "directions": [
          "query_to_doc"
      ],
      "partition_mode": "joint",
      "hardness_mode": null,
      "hardness_strength": 0.0
  }
  

Training Hyperparameters

Non-Default Hyperparameters

• eval_strategy: steps
• per_device_train_batch_size: 16
• per_device_eval_batch_size: 16
• gradient_accumulation_steps: 4
• learning_rate: 1e-05
• weight_decay: 0.01
• num_train_epochs: 1
• lr_scheduler_type: cosine
• warmup_steps: 0.1

All Hyperparameters

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

• do_predict: False
• eval_strategy: steps
• prediction_loss_only: True
• per_device_train_batch_size: 16
• per_device_eval_batch_size: 16
• gradient_accumulation_steps: 4
• eval_accumulation_steps: None
• torch_empty_cache_steps: None
• learning_rate: 1e-05
• weight_decay: 0.01
• 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: cosine
• lr_scheduler_kwargs: None
• warmup_ratio: None
• warmup_steps: 0.1
• log_level: passive
• log_level_replica: warning
• log_on_each_node: True
• logging_nan_inf_filter: True
• enable_jit_checkpoint: False
• save_on_each_node: False
• save_only_model: False
• restore_callback_states_from_checkpoint: False
• use_cpu: False
• seed: 42
• data_seed: None
• bf16: False
• fp16: False
• bf16_full_eval: False
• fp16_full_eval: False
• tf32: None
• local_rank: -1
• ddp_backend: None
• debug: []
• dataloader_drop_last: False
• dataloader_num_workers: 0
• dataloader_prefetch_factor: None
• disable_tqdm: False
• remove_unused_columns: True
• label_names: None
• load_best_model_at_end: False
• ignore_data_skip: False
• fsdp: []
• fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
• accelerator_config: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
• parallelism_config: None
• deepspeed: None
• label_smoothing_factor: 0.0
• optim: adamw_torch_fused
• optim_args: None
• group_by_length: False
• length_column_name: length
• project: huggingface
• trackio_space_id: trackio
• 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
• 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_for_metrics: []
• eval_do_concat_batches: True
• auto_find_batch_size: False
• full_determinism: False
• ddp_timeout: 1800
• torch_compile: False
• torch_compile_backend: None
• torch_compile_mode: None
• include_num_input_tokens_seen: no
• 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: True
• use_cache: False
• prompts: None
• batch_sampler: batch_sampler
• multi_dataset_batch_sampler: proportional
• router_mapping: {}
• learning_rate_mapping: {}

</details>

Training Logs

Training Time

• Training: 4.2 minutes
• Evaluation: 2.4 seconds
• Total: 4.3 minutes

Framework Versions

• Python: 3.12.13
• Sentence Transformers: 5.4.0
• Transformers: 5.0.0
• PyTorch: 2.10.0+cu128
• Accelerate: 1.13.0
• Datasets: 4.0.0
• Tokenizers: 0.22.2

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",
}

MultipleNegativesRankingLoss

@misc{oord2019representationlearningcontrastivepredictive,
      title={Representation Learning with Contrastive Predictive Coding},
      author={Aaron van den Oord and Yazhe Li and Oriol Vinyals},
      year={2019},
      eprint={1807.03748},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/1807.03748},
}

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