Multilingual base soil embedding model (quantized)
This is a sentence-transformers model finetuned from intfloat/multilingual-e5-small. 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: intfloat/multilingual-e5-small
- Maximum Sequence Length: 512 tokens
- Output Dimensionality: 384 tokens
- Similarity Function: Cosine Similarity
- Language: multilingual
- License: apache-2.0
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: 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.
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("ValentinaKim/Multilingual-base-soil-embedding")
# Run inference
sentences = [
'U-205200',
'올레핀 송유/동력 Nitrogen Section',
'차단기, 스위치류 , 스위치',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
Evaluation
Metrics
Information Retrieval
- Dataset:
dim_256
- Evaluated with
InformationRetrievalEvaluator
Metric | Value |
---|---|
cosine_accuracy@1 | 0.2442 |
cosine_accuracy@3 | 0.3101 |
cosine_accuracy@5 | 0.3643 |
cosine_accuracy@10 | 0.4109 |
cosine_precision@1 | 0.2442 |
cosine_precision@3 | 0.1034 |
cosine_precision@5 | 0.0729 |
cosine_precision@10 | 0.0411 |
cosine_recall@1 | 0.2442 |
cosine_recall@3 | 0.3101 |
cosine_recall@5 | 0.3643 |
cosine_recall@10 | 0.4109 |
cosine_ndcg@10 | 0.3172 |
cosine_mrr@10 | 0.2884 |
cosine_map@100 | 0.3003 |
Information Retrieval
- Dataset:
dim_128
- Evaluated with
InformationRetrievalEvaluator
Metric | Value |
---|---|
cosine_accuracy@1 | 0.2054 |
cosine_accuracy@3 | 0.2829 |
cosine_accuracy@5 | 0.3178 |
cosine_accuracy@10 | 0.3837 |
cosine_precision@1 | 0.2054 |
cosine_precision@3 | 0.0943 |
cosine_precision@5 | 0.0636 |
cosine_precision@10 | 0.0384 |
cosine_recall@1 | 0.2054 |
cosine_recall@3 | 0.2829 |
cosine_recall@5 | 0.3178 |
cosine_recall@10 | 0.3837 |
cosine_ndcg@10 | 0.2851 |
cosine_mrr@10 | 0.2547 |
cosine_map@100 | 0.2653 |
Information Retrieval
- Dataset:
dim_64
- Evaluated with
InformationRetrievalEvaluator
Metric | Value |
---|---|
cosine_accuracy@1 | 0.1938 |
cosine_accuracy@3 | 0.2713 |
cosine_accuracy@5 | 0.2984 |
cosine_accuracy@10 | 0.3488 |
cosine_precision@1 | 0.1938 |
cosine_precision@3 | 0.0904 |
cosine_precision@5 | 0.0597 |
cosine_precision@10 | 0.0349 |
cosine_recall@1 | 0.1938 |
cosine_recall@3 | 0.2713 |
cosine_recall@5 | 0.2984 |
cosine_recall@10 | 0.3488 |
cosine_ndcg@10 | 0.2647 |
cosine_mrr@10 | 0.2385 |
cosine_map@100 | 0.2482 |
Training Details
Training Dataset
Unnamed Dataset
- Size: 2,320 training samples
- Columns:
anchor
andpositive
- Approximate statistics based on the first 1000 samples:
anchor positive type string string details - min: 3 tokens
- mean: 6.72 tokens
- max: 16 tokens
- min: 3 tokens
- mean: 35.77 tokens
- max: 408 tokens
- Samples:
anchor positive Deionizer
탈이온장치 ; Demineralizer와 동일
Sub-CC; sub-contracting
committee외주 계약의 투명성과 공정성을 확보하기 위한 Sub-계약위원회로서 위원 및 위원
장은 CEO가 임명한다. CC이원원 부문장 이상 임원으로 하고 간사는 구매관리팀
장이 한다.In-line Sampler
원유 속의 물과 침전물의 함량을 측정하기 위하여 원유하역 Line에 설치해 놓은
시료채취기 - Loss:
MatryoshkaLoss
with these parameters:{ "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 256, 128, 64 ], "matryoshka_weights": [ 1, 1, 1 ], "n_dims_per_step": -1 }
Training Hyperparameters
Non-Default Hyperparameters
eval_strategy
: epochper_device_train_batch_size
: 32per_device_eval_batch_size
: 16gradient_accumulation_steps
: 16learning_rate
: 2e-05num_train_epochs
: 10lr_scheduler_type
: cosinewarmup_ratio
: 0.1tf32
: Falseoptim
: adamw_torch_fusedbatch_sampler
: no_duplicates
All Hyperparameters
Click to expand
overwrite_output_dir
: Falsedo_predict
: Falseeval_strategy
: epochprediction_loss_only
: Trueper_device_train_batch_size
: 32per_device_eval_batch_size
: 16per_gpu_train_batch_size
: Noneper_gpu_eval_batch_size
: Nonegradient_accumulation_steps
: 16eval_accumulation_steps
: Nonelearning_rate
: 2e-05weight_decay
: 0.0adam_beta1
: 0.9adam_beta2
: 0.999adam_epsilon
: 1e-08max_grad_norm
: 1.0num_train_epochs
: 10max_steps
: -1lr_scheduler_type
: cosinelr_scheduler_kwargs
: {}warmup_ratio
: 0.1warmup_steps
: 0log_level
: passivelog_level_replica
: warninglog_on_each_node
: Truelogging_nan_inf_filter
: Truesave_safetensors
: Truesave_on_each_node
: Falsesave_only_model
: Falserestore_callback_states_from_checkpoint
: Falseno_cuda
: Falseuse_cpu
: Falseuse_mps_device
: Falseseed
: 42data_seed
: Nonejit_mode_eval
: Falseuse_ipex
: Falsebf16
: Falsefp16
: Falsefp16_opt_level
: O1half_precision_backend
: autobf16_full_eval
: Falsefp16_full_eval
: Falsetf32
: Falselocal_rank
: 0ddp_backend
: Nonetpu_num_cores
: Nonetpu_metrics_debug
: Falsedebug
: []dataloader_drop_last
: Falsedataloader_num_workers
: 0dataloader_prefetch_factor
: Nonepast_index
: -1disable_tqdm
: Falseremove_unused_columns
: Truelabel_names
: Noneload_best_model_at_end
: Falseignore_data_skip
: Falsefsdp
: []fsdp_min_num_params
: 0fsdp_config
: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}fsdp_transformer_layer_cls_to_wrap
: Noneaccelerator_config
: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}deepspeed
: Nonelabel_smoothing_factor
: 0.0optim
: adamw_torch_fusedoptim_args
: Noneadafactor
: Falsegroup_by_length
: Falselength_column_name
: lengthddp_find_unused_parameters
: Noneddp_bucket_cap_mb
: Noneddp_broadcast_buffers
: Falsedataloader_pin_memory
: Truedataloader_persistent_workers
: Falseskip_memory_metrics
: Trueuse_legacy_prediction_loop
: Falsepush_to_hub
: Falseresume_from_checkpoint
: Nonehub_model_id
: Nonehub_strategy
: every_savehub_private_repo
: Falsehub_always_push
: Falsegradient_checkpointing
: Falsegradient_checkpointing_kwargs
: Noneinclude_inputs_for_metrics
: Falseeval_do_concat_batches
: Truefp16_backend
: autopush_to_hub_model_id
: Nonepush_to_hub_organization
: Nonemp_parameters
:auto_find_batch_size
: Falsefull_determinism
: Falsetorchdynamo
: Noneray_scope
: lastddp_timeout
: 1800torch_compile
: Falsetorch_compile_backend
: Nonetorch_compile_mode
: Nonedispatch_batches
: Nonesplit_batches
: Noneinclude_tokens_per_second
: Falseinclude_num_input_tokens_seen
: Falseneftune_noise_alpha
: Noneoptim_target_modules
: Nonebatch_eval_metrics
: Falsebatch_sampler
: no_duplicatesmulti_dataset_batch_sampler
: proportional
Training Logs
Epoch | Step | Training Loss | dim_128_cosine_map@100 | dim_256_cosine_map@100 | dim_64_cosine_map@100 |
---|---|---|---|---|---|
0.8767 | 4 | - | 0.2156 | 0.2448 | 0.1831 |
1.9726 | 9 | - | 0.2511 | 0.2765 | 0.2154 |
2.1918 | 10 | 7.6309 | - | - | - |
2.8493 | 13 | - | 0.2531 | 0.2852 | 0.2345 |
3.9452 | 18 | - | 0.2617 | 0.2914 | 0.2353 |
4.3836 | 20 | 5.3042 | - | - | - |
4.8219 | 22 | - | 0.2626 | 0.2946 | 0.2422 |
5.9178 | 27 | - | 0.2629 | 0.2987 | 0.2481 |
6.5753 | 30 | 4.2433 | - | - | - |
6.7945 | 31 | - | 0.2684 | 0.2988 | 0.2495 |
7.8904 | 36 | - | 0.2652 | 0.3003 | 0.2488 |
8.7671 | 40 | 3.9117 | 0.2653 | 0.3003 | 0.2482 |
Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.1.1
- Transformers: 4.41.2
- PyTorch: 2.1.2+cu121
- Accelerate: 1.0.0
- Datasets: 2.19.1
- Tokenizers: 0.19.1
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",
}
MatryoshkaLoss
@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
@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}
}
- Downloads last month
- 14
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social
visibility and check back later, or deploy to Inference Endpoints (dedicated)
instead.
Model tree for ValentinaKim/Multilingual-base-soil-embedding
Base model
intfloat/multilingual-e5-smallEvaluation results
- Cosine Accuracy@1 on dim 256self-reported0.244
- Cosine Accuracy@3 on dim 256self-reported0.310
- Cosine Accuracy@5 on dim 256self-reported0.364
- Cosine Accuracy@10 on dim 256self-reported0.411
- Cosine Precision@1 on dim 256self-reported0.244
- Cosine Precision@3 on dim 256self-reported0.103
- Cosine Precision@5 on dim 256self-reported0.073
- Cosine Precision@10 on dim 256self-reported0.041
- Cosine Recall@1 on dim 256self-reported0.244
- Cosine Recall@3 on dim 256self-reported0.310