SentenceTransformer based on sentence-transformers/all-mpnet-base-v2
This is a sentence-transformers model finetuned from sentence-transformers/all-mpnet-base-v2. It maps sentences & paragraphs to a 768-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-mpnet-base-v2
- Maximum Sequence Length: 384 tokens
- Output Dimensionality: 768 tokens
- Similarity Function: Cosine Similarity
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': 384, 'do_lower_case': False}) with Transformer model: MPNetModel
(1): Pooling({'word_embedding_dimension': 768, '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("alpcansoydas/product-model-16.10.24-ifhavemorethan10sampleperfamily")
# Run inference
sentences = [
'Power Cable,600V/1000V,ROV-K,4mm^2,Black Jacket(The Color Of Core Is Blue And Brown),36A,Shielded Style Outdoor Cable',
'Electrical equipment and components and supplies',
'Power sources',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
Evaluation
Metrics
Semantic Similarity
- Evaluated with
EmbeddingSimilarityEvaluator
Metric | Value |
---|---|
pearson_cosine | nan |
spearman_cosine | nan |
pearson_manhattan | nan |
spearman_manhattan | nan |
pearson_euclidean | nan |
spearman_euclidean | nan |
pearson_dot | nan |
spearman_dot | nan |
pearson_max | nan |
spearman_max | nan |
Training Details
Training Dataset
Unnamed Dataset
- Size: 25,110 training samples
- Columns:
sentence1
andsentence2
- Approximate statistics based on the first 1000 samples:
sentence1 sentence2 type string string details - min: 3 tokens
- mean: 17.04 tokens
- max: 83 tokens
- min: 3 tokens
- mean: 7.97 tokens
- max: 12 tokens
- Samples:
sentence1 sentence2 USRC20(RH2288,2E5-2680v2,1616G,12600GB(2.5 )+2600GB(2.5 ),410GE,4GE,DC)-OS RAID1,DATA RAID5+Hotspare,No DVDRW
Computer Equipment and Accessories
100m 160x10 Kafes Kule
Heavy construction machinery and equipment
Air4820 Superonline Video Bridge
Data Voice or Multimedia Network Equipment or Platforms and Accessories
- Loss:
MultipleNegativesRankingLoss
with these parameters:{ "scale": 20.0, "similarity_fct": "cos_sim" }
Evaluation Dataset
Unnamed Dataset
- Size: 5,381 evaluation samples
- Columns:
sentence1
andsentence2
- Approximate statistics based on the first 1000 samples:
sentence1 sentence2 type string string details - min: 3 tokens
- mean: 16.75 tokens
- max: 71 tokens
- min: 3 tokens
- mean: 7.89 tokens
- max: 12 tokens
- Samples:
sentence1 sentence2 SNTC-24X7X4 Cisco ISR 4331 (2GE,2NIM,4G FLASH,4G DRA
Data Voice or Multimedia Network Equipment or Platforms and Accessories
Iridium GO Ecex
Communications Devices and Accessories
LC/LC SM 9/125 DX 1.8mm Lszh L 10m
Components for information technology or broadcasting or telecommunications
- Loss:
MultipleNegativesRankingLoss
with these parameters:{ "scale": 20.0, "similarity_fct": "cos_sim" }
Training Hyperparameters
Non-Default Hyperparameters
eval_strategy
: stepsper_device_train_batch_size
: 16per_device_eval_batch_size
: 16num_train_epochs
: 2warmup_ratio
: 0.1fp16
: True
All Hyperparameters
Click to expand
overwrite_output_dir
: Falsedo_predict
: Falseeval_strategy
: stepsprediction_loss_only
: Trueper_device_train_batch_size
: 16per_device_eval_batch_size
: 16per_gpu_train_batch_size
: Noneper_gpu_eval_batch_size
: Nonegradient_accumulation_steps
: 1eval_accumulation_steps
: Nonetorch_empty_cache_steps
: Nonelearning_rate
: 5e-05weight_decay
: 0.0adam_beta1
: 0.9adam_beta2
: 0.999adam_epsilon
: 1e-08max_grad_norm
: 1.0num_train_epochs
: 2max_steps
: -1lr_scheduler_type
: linearlr_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
: Truefp16_opt_level
: O1half_precision_backend
: autobf16_full_eval
: Falsefp16_full_eval
: Falsetf32
: Nonelocal_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_torchoptim_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
: Falseeval_on_start
: Falseeval_use_gather_object
: Falsebatch_sampler
: batch_samplermulti_dataset_batch_sampler
: proportional
Training Logs
Epoch | Step | Training Loss | Validation Loss | spearman_max |
---|---|---|---|---|
0.0637 | 100 | 2.2804 | 1.9512 | nan |
0.1274 | 200 | 1.8803 | 1.9189 | nan |
0.1911 | 300 | 1.8687 | 1.7873 | nan |
0.2548 | 400 | 1.7455 | 1.7351 | nan |
0.3185 | 500 | 1.714 | 1.6717 | nan |
0.3822 | 600 | 1.6956 | 1.6789 | nan |
0.4459 | 700 | 1.7134 | 1.6407 | nan |
0.5096 | 800 | 1.7059 | 1.6175 | nan |
0.5732 | 900 | 1.674 | 1.6256 | nan |
0.6369 | 1000 | 1.6725 | 1.5826 | nan |
0.7006 | 1100 | 1.6238 | 1.5815 | nan |
0.7643 | 1200 | 1.5819 | 1.5684 | nan |
0.8280 | 1300 | 1.526 | 1.5511 | nan |
0.8917 | 1400 | 1.4976 | 1.5496 | nan |
0.9554 | 1500 | 1.5709 | 1.5358 | nan |
1.0191 | 1600 | 1.4731 | 1.5498 | nan |
1.0828 | 1700 | 1.3914 | 1.5280 | nan |
1.1465 | 1800 | 1.4137 | 1.4980 | nan |
1.2102 | 1900 | 1.3964 | 1.5012 | nan |
1.2739 | 2000 | 1.4244 | 1.4972 | nan |
1.3376 | 2100 | 1.4567 | 1.4943 | nan |
1.4013 | 2200 | 1.4224 | 1.4880 | nan |
1.4650 | 2300 | 1.4452 | 1.4685 | nan |
1.5287 | 2400 | 1.3843 | 1.4976 | nan |
1.5924 | 2500 | 1.4538 | 1.4715 | nan |
1.6561 | 2600 | 1.3864 | 1.4738 | nan |
1.7197 | 2700 | 1.3514 | 1.4724 | nan |
1.7834 | 2800 | 1.4295 | 1.4538 | nan |
1.8471 | 2900 | 1.3631 | 1.4629 | nan |
1.9108 | 3000 | 1.3654 | 1.4588 | nan |
1.9745 | 3100 | 1.3335 | 1.4552 | nan |
Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.2.0
- Transformers: 4.44.2
- PyTorch: 2.4.1+cu121
- Accelerate: 0.34.2
- Datasets: 3.0.1
- 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",
}
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}
}
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sentence-transformers/all-mpnet-base-v2Evaluation results
- Pearson Cosine on Unknownself-reportedNaN
- Spearman Cosine on Unknownself-reportedNaN
- Pearson Manhattan on Unknownself-reportedNaN
- Spearman Manhattan on Unknownself-reportedNaN
- Pearson Euclidean on Unknownself-reportedNaN
- Spearman Euclidean on Unknownself-reportedNaN
- Pearson Dot on Unknownself-reportedNaN
- Spearman Dot on Unknownself-reportedNaN
- Pearson Max on Unknownself-reportedNaN
- Spearman Max on Unknownself-reportedNaN