metadata

tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:9316
  - loss:MultipleNegativesRankingLoss
base_model: intfloat/multilingual-e5-large
widget:
  - source_sentence: >-
      Horn band legwearis a type oflegwear, oftenthighhighs, with
      ahornedcharacter design along the upper band.
    sentences:
      - horn band legwear
      - head out of frame
      - sweatpants
  - source_sentence: >-
      When a character is looping the laces of theiruntied shoelacesinto a
      sturdy bow.
    sentences:
      - hair tie
      - tying footwear
      - loose necktie
  - source_sentence: >-
      Use this tag if the person's eyewear isremovedfrom their usual place and
      carried in the hands. If it still rests on the bridge of the nose or head,
      seeadjusting eyewearand its related tags.
    sentences:
      - cow costume
      - sarong
      - holding removed eyewear
  - source_sentence: When both of a character's hands are on another character'sthighs.
    sentences:
      - baking
      - triplets
      - hands on another's thighs
  - source_sentence: >-
      A long appendage protruding from the lower back. Often covered in fur or
      scales. A common feature of animal girls.
    sentences:
      - tail
      - grey-framed eyewear
      - stomach day
datasets:
  - meandyou200175/word_embedding
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
  - cosine_accuracy@1
  - cosine_accuracy@2
  - cosine_accuracy@5
  - cosine_accuracy@10
  - cosine_accuracy@100
  - cosine_precision@1
  - cosine_precision@2
  - cosine_precision@5
  - cosine_precision@10
  - cosine_precision@100
  - cosine_recall@1
  - cosine_recall@2
  - cosine_recall@5
  - cosine_recall@10
  - cosine_recall@100
  - cosine_ndcg@10
  - cosine_mrr@1
  - cosine_mrr@2
  - cosine_mrr@5
  - cosine_mrr@10
  - cosine_mrr@100
  - cosine_map@100
model-index:
  - name: SentenceTransformer based on intfloat/multilingual-e5-large
    results:
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: Unknown
          type: unknown
        metrics:
          - type: cosine_accuracy@1
            value: 0.8108108108108109
            name: Cosine Accuracy@1
          - type: cosine_accuracy@2
            value: 0.8957528957528957
            name: Cosine Accuracy@2
          - type: cosine_accuracy@5
            value: 0.9382239382239382
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.9642857142857143
            name: Cosine Accuracy@10
          - type: cosine_accuracy@100
            value: 0.9932432432432432
            name: Cosine Accuracy@100
          - type: cosine_precision@1
            value: 0.8108108108108109
            name: Cosine Precision@1
          - type: cosine_precision@2
            value: 0.44787644787644787
            name: Cosine Precision@2
          - type: cosine_precision@5
            value: 0.18764478764478765
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.09642857142857143
            name: Cosine Precision@10
          - type: cosine_precision@100
            value: 0.009932432432432433
            name: Cosine Precision@100
          - type: cosine_recall@1
            value: 0.8108108108108109
            name: Cosine Recall@1
          - type: cosine_recall@2
            value: 0.8957528957528957
            name: Cosine Recall@2
          - type: cosine_recall@5
            value: 0.9382239382239382
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.9642857142857143
            name: Cosine Recall@10
          - type: cosine_recall@100
            value: 0.9932432432432432
            name: Cosine Recall@100
          - type: cosine_ndcg@10
            value: 0.8923095558988695
            name: Cosine Ndcg@10
          - type: cosine_mrr@1
            value: 0.8108108108108109
            name: Cosine Mrr@1
          - type: cosine_mrr@2
            value: 0.8532818532818532
            name: Cosine Mrr@2
          - type: cosine_mrr@5
            value: 0.8649292149292154
            name: Cosine Mrr@5
          - type: cosine_mrr@10
            value: 0.8687695348409635
            name: Cosine Mrr@10
          - type: cosine_mrr@100
            value: 0.8700193430588538
            name: Cosine Mrr@100
          - type: cosine_map@100
            value: 0.8700193430588539
            name: Cosine Map@100

SentenceTransformer based on intfloat/multilingual-e5-large

This is a sentence-transformers model finetuned from intfloat/multilingual-e5-large on the word_embedding dataset. It maps sentences & paragraphs to a 1024-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: intfloat/multilingual-e5-large
Maximum Sequence Length: 512 tokens
Output Dimensionality: 1024 dimensions
Similarity Function: Cosine Similarity
Training Dataset:
- word_embedding

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': 512, 'do_lower_case': False}) with Transformer model: XLMRobertaModel 
  (1): Pooling({'word_embedding_dimension': 1024, '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.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("meandyou200175/e5_large_finetune_word")
# Run inference
sentences = [
    'A long appendage protruding from the lower back. Often covered in fur or scales. A common feature of animal girls.',
    'tail',
    'stomach day',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]

Evaluation

Metrics

Information Retrieval

Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.8108
cosine_accuracy@2	0.8958
cosine_accuracy@5	0.9382
cosine_accuracy@10	0.9643
cosine_accuracy@100	0.9932
cosine_precision@1	0.8108
cosine_precision@2	0.4479
cosine_precision@5	0.1876
cosine_precision@10	0.0964
cosine_precision@100	0.0099
cosine_recall@1	0.8108
cosine_recall@2	0.8958
cosine_recall@5	0.9382
cosine_recall@10	0.9643
cosine_recall@100	0.9932
cosine_ndcg@10	0.8923
cosine_mrr@1	0.8108
cosine_mrr@2	0.8533
cosine_mrr@5	0.8649
cosine_mrr@10	0.8688
cosine_mrr@100	0.87
cosine_map@100	0.87

Training Details

Training Dataset

word_embedding

Dataset: word_embedding at af76b11
Size: 9,316 training samples
Columns: query and positive
Approximate statistics based on the first 1000 samples:
query positive
type string string
details
min: 3 tokens
mean: 36.54 tokens
max: 177 tokens

min: 3 tokens
mean: 5.3 tokens
max: 13 tokens

	query	positive
type	string	string
details	min: 3 tokens mean: 36.54 tokens max: 177 tokens	min: 3 tokens mean: 5.3 tokens max: 13 tokens

Samples:

query	positive
`Eyewear shaped like a semicircle.`	`semi-circular eyewear`
`A handheld electric appliance used fordryingand styling hair.`	`hair dryer`
`When onebreastis exposed while the other remains covered or confined by clothing. Seebreasts outfor when both breasts are exposed.`	`one breast out`

Loss: MultipleNegativesRankingLoss with these parameters:

{
    "scale": 20.0,
    "similarity_fct": "cos_sim"
}

Evaluation Dataset

word_embedding

Dataset: word_embedding at af76b11
Size: 1,036 evaluation samples
Columns: query and positive
Approximate statistics based on the first 1000 samples:
query positive
type string string
details
min: 4 tokens
mean: 35.89 tokens
max: 164 tokens

min: 3 tokens
mean: 5.38 tokens
max: 14 tokens

	query	positive
type	string	string
details	min: 4 tokens mean: 35.89 tokens max: 164 tokens	min: 3 tokens mean: 5.38 tokens max: 14 tokens

Samples:

query	positive
`A machine that manipulates data according to a list of instructions. The ability to store and execute lists of instructions called programs make computers extremely versatile. On Danbooru's images they are most often used fordrawing,playing gamesand accessing theinternet.`	`computer`
`Aplaying cardwith twoclubs.`	`two of clubs`
`Yebisu (ヱビス, Ebisu) is a beer produced bySapporo Breweries. It is one of Japan's oldest brands, first being brewed in Tokyo in 1890 by the Japan Beer Brewery Company.`	`yebisu`

Loss: MultipleNegativesRankingLoss with these parameters:

{
    "scale": 20.0,
    "similarity_fct": "cos_sim"
}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 16
per_device_eval_batch_size: 16
learning_rate: 2e-05
num_train_epochs: 5
warmup_ratio: 0.1
fp16: True
batch_sampler: no_duplicates

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: steps
prediction_loss_only: True
per_device_train_batch_size: 16
per_device_eval_batch_size: 16
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: 5
max_steps: -1
lr_scheduler_type: linear
lr_scheduler_kwargs: {}
warmup_ratio: 0.1
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: 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}
tp_size: 0
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}
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: no_duplicates
multi_dataset_batch_sampler: proportional

Training Logs

Epoch	Step	Training Loss	Validation Loss	cosine_ndcg@10
-1	-1	-	-	0.7166
0.1715	100	0.8892	-	-
0.3431	200	0.1724	-	-
0.5146	300	0.1783	-	-
0.6861	400	0.1393	-	-
0.8576	500	0.1262	-	-
1.0292	600	0.1046	-	-
1.2007	700	0.0639	-	-
1.3722	800	0.0692	-	-
1.5437	900	0.043	-	-
1.7153	1000	0.0614	0.0819	0.8774
1.8868	1100	0.0538	-	-
2.0583	1200	0.0414	-	-
2.2298	1300	0.0146	-	-
2.4014	1400	0.0164	-	-
2.5729	1500	0.0225	-	-
2.7444	1600	0.0215	-	-
2.9160	1700	0.0271	-	-
3.0875	1800	0.0202	-	-
3.2590	1900	0.0194	-	-
3.4305	2000	0.0144	0.0682	0.8923
3.6021	2100	0.0118	-	-
3.7736	2200	0.0155	-	-
3.9451	2300	0.0177	-	-
4.1166	2400	0.0059	-	-
4.2882	2500	0.0099	-	-
4.4597	2600	0.0056	-	-
4.6312	2700	0.0153	-	-
4.8027	2800	0.0069	-	-
4.9743	2900	0.01	-	-

Framework Versions

Python: 3.11.11
Sentence Transformers: 3.4.1
Transformers: 4.51.1
PyTorch: 2.5.1+cu124
Accelerate: 1.3.0
Datasets: 3.5.0
Tokenizers: 0.21.0

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{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}
}