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franzclarin/ReverseDictionary

sourceHugging Faceupdated 1mo agoView on Hugging Face

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Model Card

SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2

This is a sentence-transformers model finetuned from sentence-transformers/all-MiniLM-L6-v2. It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for retrieval.

Model Details

Model Description

—Model Type: Sentence Transformer
—Base model: sentence-transformers/all-MiniLM-L6-v2
—Maximum Sequence Length: 256 tokens
—Output Dimensionality: 384 dimensions
—Similarity Function: Cosine Similarity
—Supported Modality: Text

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': 'BertModel'})
  (1): Pooling({'embedding_dimension': 384, 'pooling_mode': 'mean', 'include_prompt': True})
  (2): Normalize({})
)

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("sentence_transformers_model_id")
# Run inference
sentences = [
    'someone who is dazzlingly skilled in any field',
    'champion',
    'champion lode',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.4228, 0.3128],
#         [0.4228, 1.0000, 0.5602],
#         [0.3128, 0.5602, 1.0000]])

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Downstream Usage (Sentence Transformers)

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Training Details

Training Dataset

Unnamed Dataset

—Size: 181,149 training samples
—Columns: <code>sentence0</code>, <code>sentence1</code>, and <code>sentence_2</code>
—Approximate statistics based on the first 100 samples: | | sentence0 | sentence1 | sentence_2 | |:---------|:----------------------------------------------------------------------------------|:--------------------------------------------------------------------------------|:--------------------------------------------------------------------------------| | type | string | string | string | | modality | text | text | text | | details | <ul><li>min: 5 tokens</li><li>mean: 13.67 tokens</li><li>max: 39 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 4.63 tokens</li><li>max: 9 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 4.48 tokens</li><li>max: 9 tokens</li></ul> |
—Samples: | sentence0 | sentence1 | sentence_2 | |:------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------|:------------------------------| | <code>short hairy perennial with early spring blue-violet or lilac flowers; North America and Siberia</code> | <code>American pulsatilla</code> | <code>genus Pulsatilla</code> | | <code>of or relating to or characteristic of Damascus or its people</code> | <code>damascene</code> | <code>Damascene</code> | | <code>United States Arctic explorer and United States naval officer who has been regarded as the first man to reach the North Pole (1856-1920)</code> | <code>Peary</code> | <code>pear tree</code> |
—Loss: <code>MultipleNegativesRankingLoss</code> with these parameters:

json

  {
      "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

—per_device_train_batch_size: 64
—fp16: True
—per_device_eval_batch_size: 64
—multi_dataset_batch_sampler: round_robin

All Hyperparameters

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

—per_device_train_batch_size: 64
—num_train_epochs: 3
—max_steps: -1
—learning_rate: 5e-05
—lr_scheduler_type: linear
—lr_scheduler_kwargs: None
—warmup_steps: 0
—optim: adamwtorchfused
—optim_args: None
—weight_decay: 0.0
—adam_beta1: 0.9
—adam_beta2: 0.999
—adam_epsilon: 1e-08
—optim_target_modules: None
—gradient_accumulation_steps: 1
—average_tokens_across_devices: True
—max_grad_norm: 1
—label_smoothing_factor: 0.0
—bf16: False
—fp16: True
—bf16_full_eval: False
—fp16_full_eval: False
—tf32: None
—gradient_checkpointing: False
—gradient_checkpointing_kwargs: None
—torch_compile: False
—torch_compile_backend: None
—torch_compile_mode: None
—use_liger_kernel: False
—liger_kernel_config: None
—use_cache: False
—neftune_noise_alpha: None
—torch_empty_cache_steps: None
—auto_find_batch_size: False
—log_on_each_node: True
—logging_nan_inf_filter: True
—include_num_input_tokens_seen: no
—log_level: passive
—log_level_replica: warning
—disable_tqdm: False
—project: huggingface
—trackio_space_id: None
—trackio_bucket_id: None
—trackio_static_space_id: None
—per_device_eval_batch_size: 64
—prediction_loss_only: True
—eval_on_start: False
—eval_do_concat_batches: True
—eval_use_gather_object: False
—eval_accumulation_steps: None
—include_for_metrics: []
—batch_eval_metrics: False
—save_only_model: False
—save_on_each_node: False
—enable_jit_checkpoint: False
—push_to_hub: False
—hub_private_repo: None
—hub_model_id: None
—hub_strategy: every_save
—hub_always_push: False
—hub_revision: None
—load_best_model_at_end: False
—ignore_data_skip: False
—restore_callback_states_from_checkpoint: False
—full_determinism: False
—seed: 42
—data_seed: None
—use_cpu: False
—accelerator_config: {'splitbatches': False, 'dispatchbatches': None, 'evenbatches': True, 'useseedablesampler': True, 'nonblocking': False, 'gradientaccumulationkwargs': None}
—parallelism_config: None
—dataloader_drop_last: False
—dataloader_num_workers: 0
—dataloader_pin_memory: True
—dataloader_persistent_workers: False
—dataloader_prefetch_factor: None
—remove_unused_columns: True
—label_names: None
—train_sampling_strategy: random
—length_column_name: length
—ddp_find_unused_parameters: None
—ddp_bucket_cap_mb: None
—ddp_broadcast_buffers: False
—ddp_static_graph: None
—ddp_backend: None
—ddp_timeout: 1800
—fsdp: None
—fsdp_config: None
—deepspeed: None
—debug: []
—skip_memory_metrics: True
—do_predict: False
—resume_from_checkpoint: None
—warmup_ratio: None
—local_rank: -1
—prompts: None
—batch_sampler: batch_sampler
—multi_dataset_batch_sampler: round_robin
—router_mapping: {}
—learning_rate_mapping: {}

</details>

Training Logs

Epoch	Step	Training Loss
0.1766	500	2.3408
0.3532	1000	2.0104
0.5298	1500	1.8874
0.7065	2000	1.8023
0.8831	2500	1.7617
1.0597	3000	1.6927
1.2363	3500	1.6037
1.4129	4000	1.5911
1.5895	4500	1.5621
1.7662	5000	1.5638
1.9428	5500	1.5497
2.1194	6000	1.4927
2.2960	6500	1.4566
2.4726	7000	1.4559
2.6492	7500	1.4507
2.8259	8000	1.4514

Training Time

—Training: 18.0 minutes

Framework Versions

—Python: 3.12.13
—Sentence Transformers: 5.6.0
—Transformers: 5.13.1
—PyTorch: 2.11.0+cu128
—Accelerate: 1.14.0
—Datasets: 5.0.1
—Tokenizers: 0.22.2

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",
}

MultipleNegativesRankingLoss

bibtex

@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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