pameydorke/arcanum-quests-retriever-v2

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-v2")
# Run inference
sentences = [
    'dernholm marry lady druella task guide',
    "Adkin Chambers and Sir Garrick Stout. Melee Mastery only. This path is for players who only desire Melee Mastery. However they may still obtain Dodge Mastery later (see path 3) Seek out Sir Garrick Stout in Dernholm Accept his quest to Find Lady Druella with an offer of marriage and return her to Dernholm The offer comes with a bribe of a potion to restore the sight of the blinded Adkin Chambers Go to the Gyr Dolours' lair which Stout marked on your map Rescue Lady Druella without her being killed You can convince Druella to return if you tell her (lie or the truth) that you will kill Stout as soon as he provides the Potion Return to Dernholm and present Druella to Stout Stout will then give Druella the Potion; and your reward is Master Training and 8,500 xp",
    "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",
]
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, 0.5645, 0.3405],
#         [0.5645, 1.0000, 0.3979],
#         [0.3405, 0.3979, 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: 246.84 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>The Main Quest. Getting Information at P. Schuyler and Sons. P. Schuyler and Sons is at 44 Devonshire Way. Inside you find James Kingford who fobs you off. To continue, you can: Kill him, quick and messy Pick pocket his key Convince him that you belong to the Tarant Authorities and he needs to cooperate (which can lead to his death) You can then use it to go down the trapdoor in the back room. This leads to a room full of zombies and some random treasure. Once the zombies are dispatched, you can go down the next trapdoor. This leads to a tunnel with even more zombies and traps. Go down the next trapdoor. Here you will find a dwarven tomb. You will find the Schuylers here. To get the information you require you can Kill them all, find they key and open the wood chest Use your pick pocket skill to get the key Come to an agreement with them to get the information (this will annoy Magnus) Use you pick locks skill to open the wood chest. In the end, you find that the ring belongs to Gilbert...</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>Find Vegard's Family Heirloom. Receive the quest from Vegard MoltenFlow. Go down into The Dredge. You will eventually come across a room to the right side of a tunnel. The heirloom is in an unlocked chest inside, but the door leading inside is locked. You can either... Navigate through the dark parts of the Dredge further ahead and find the key to the door lying on the ground. Destroy the door or pick its lock. Return the heirloom to Vegard to receive your rewards.</code> |
• —Loss: <code>GISTEmbedLoss</code> with these parameters:

  {
      "guide": "SentenceTransformer('BAAI/bge-m3')",
      "temperature": 0.07,
      "margin_strategy": "absolute",
      "margin": 0.0,
      "contrast_anchors": true,
      "contrast_positives": true,
      "gather_across_devices": false
  }

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
• —batch_sampler: no_duplicates

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: {'minnumparams': 0, 'xla': False, 'xlafsdpv2': False, 'xlafsdpgrad_ckpt': False}
• —accelerator_config: {'splitbatches': False, 'dispatchbatches': None, 'evenbatches': True, 'useseedablesampler': True, 'nonblocking': False, 'gradientaccumulationkwargs': None}
• —parallelism_config: None
• —deepspeed: None
• —label_smoothing_factor: 0.0
• —optim: adamwtorchfused
• —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: no_duplicates
• —multi_dataset_batch_sampler: proportional
• —router_mapping: {}
• —learning_rate_mapping: {}

</details>

Training Logs

Training Time

• —Training: 6.3 minutes
• —Evaluation: 2.3 seconds
• —Total: 6.3 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",
}

GISTEmbedLoss

@misc{solatorio2024gistembed,
    title={GISTEmbed: Guided In-sample Selection of Training Negatives for Text Embedding Fine-tuning},
    author={Aivin V. Solatorio},
    year={2024},
    eprint={2402.16829},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}

<!--

Glossary

Clearly define terms in order to be accessible across audiences. -->

<!--

Model Card Authors

Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction. -->

<!--

Model Card Contact

Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors. -->