This is a sentence-transformers model finetuned from sentence-transformers/all-MiniLM-L6-v2 on the csv dataset. It maps sentences & paragraphs to a 384-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/all-MiniLM-L6-v2
Maximum Sequence Length: 256 tokens
Output Dimensionality: 384 dimensions
Similarity Function: Cosine Similarity
Training Dataset:
- csv

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': 256, 'do_lower_case': False, 'architecture': 'BertModel'})
  (1): Pooling({'word_embedding_dimension': 384, '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})
  (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.

python

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("Mehularora/scrabble-embed-v1")
# Run inference
sentences = [
    'BOLDING',
    '< BOLD, confident and fearless [adj]',
    '< NAUCH, nautch (intricate traditional Indian dance) [n]',
]
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, 0.7391, 0.0112],
#         [0.7391, 1.0000, 0.0722],
#         [0.0112, 0.0722, 1.0000]])

Evaluation

Metrics

Information Retrieval

Dataset: dictionary-test
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.597
cosine_accuracy@3	0.7253
cosine_accuracy@5	0.7496
cosine_accuracy@10	0.7743
cosine_precision@1	0.597
cosine_precision@3	0.2418
cosine_precision@5	0.1499
cosine_precision@10	0.0774
cosine_recall@1	0.597
cosine_recall@3	0.7253
cosine_recall@5	0.7496
cosine_recall@10	0.7743
cosine_ndcg@10	0.6919
cosine_mrr@10	0.6649
cosine_map@100	0.6677

Training Details

Training Dataset

csv

Dataset: csv
Size: 227,518 training samples
Columns: word and definition
Approximate statistics based on the first 1000 samples:
word definition
type string string
details
min: 3 tokens
mean: 4.9 tokens
max: 9 tokens
min: 6 tokens
mean: 15.82 tokens
max: 44 tokens
Samples:
word definition
SLURPIEST < SLURPY, making a slurping noise [adj]
CRISPNESSES < CRISPNESS, < CRISP, fresh and firm [adj]
CECUTIENCY a tendency to blindness [n]
Loss: MatryoshkaLoss with these parameters:
json
1{ 2 "loss": "MultipleNegativesRankingLoss", 3 "matryoshka_dims": [ 4 384, 5 256 6 ], 7 "matryoshka_weights": [ 8 1, 9 1 10 ], 11 "n_dims_per_step": -1 12}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 64
learning_rate: 2e-05
num_train_epochs: 1
fp16: True

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: steps
prediction_loss_only: True
per_device_train_batch_size: 64
per_device_eval_batch_size: 8
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: 2e-05
weight_decay: 0.0
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: 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
bf16: False
fp16: True
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: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
fsdp_transformer_layer_cls_to_wrap: None
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
adafactor: False
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
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: 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
prompts: None
batch_sampler: batch_sampler
multi_dataset_batch_sampler: proportional
router_mapping: {}
learning_rate_mapping: {}

Training Logs

Epoch	Step	Training Loss	dictionary-test_cosine_ndcg@10
0.0281	100	1.5353	0.6306
0.0563	200	1.2836	0.6543
0.0844	300	1.2305	0.6637
0.1125	400	1.1669	0.6651
0.1406	500	1.1904	0.6714
0.1688	600	1.0998	0.6738
0.1969	700	1.0655	0.6751
0.2250	800	1.095	0.6781
0.2532	900	1.1535	0.6813
0.2813	1000	1.0047	0.6814
0.3094	1100	1.0749	0.6809
0.3376	1200	1.0642	0.6813
0.3657	1300	1.0718	0.6851
0.3938	1400	1.023	0.6854
0.4219	1500	1.0429	0.6850
0.4501	1600	1.0088	0.6849
0.4782	1700	1.0129	0.6873
0.5063	1800	0.988	0.6874
0.5345	1900	1.0413	0.6882
0.5626	2000	1.0043	0.6885
0.5907	2100	0.9929	0.6886
0.6188	2200	0.9403	0.6899
0.6470	2300	0.9789	0.6907
0.6751	2400	0.9595	0.6912
0.7032	2500	0.9786	0.6914
0.7314	2600	0.9647	0.6911
0.7595	2700	0.9245	0.6897
0.7876	2800	0.9685	0.6906
0.8158	2900	0.9778	0.6896
0.8439	3000	0.939	0.6906
0.8720	3100	0.9822	0.6904
0.9001	3200	1.0038	0.6913
0.9283	3300	0.9297	0.6910
0.9564	3400	0.9215	0.6915
0.9845	3500	0.948	0.6919

Framework Versions

Python: 3.11.4
Sentence Transformers: 5.1.2
Transformers: 4.57.3
PyTorch: 2.9.1+cpu
Accelerate: 1.12.0
Datasets: 4.4.1
Tokenizers: 0.22.1

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",
}

MatryoshkaLoss

bibtex

@misc{kusupati2024matryoshka,
    title={Matryoshka Representation Learning},
    author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
    year={2024},
    eprint={2205.13147},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}

MultipleNegativesRankingLoss

bibtex

@misc{henderson2017efficient,
    title={Efficient Natural Language Response Suggestion for Smart Reply},
    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
    year={2017},
    eprint={1705.00652},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}

	word	definition
type	string	string
details	min: 3 tokens mean: 4.9 tokens max: 9 tokens	min: 6 tokens mean: 15.82 tokens max: 44 tokens

word	definition
`SLURPIEST`	`< SLURPY, making a slurping noise [adj]`
`CRISPNESSES`	`< CRISPNESS, < CRISP, fresh and firm [adj]`
`CECUTIENCY`	`a tendency to blindness [n]`

mehularora/scrabble-embed-v1

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

Model Details

Model Description

Model Sources

Full Model Architecture

Usage

Direct Usage (Sentence Transformers)

Evaluation

Metrics

Information Retrieval

Training Details

Training Dataset

csv

Training Hyperparameters

Non-Default Hyperparameters

All Hyperparameters

Training Logs

Framework Versions

Citation

BibTeX

Sentence Transformers

MatryoshkaLoss

MultipleNegativesRankingLoss

mehularora/
scrabble-embed-v1