SentenceTransformer based on vinai/phobert-base-v2
This is a sentence-transformers model finetuned from vinai/phobert-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: vinai/phobert-base-v2
- Maximum Sequence Length: 512 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': 512, 'do_lower_case': False}) with Transformer model: RobertaModel
(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})
)
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("huudan123/stag_123_cp10000")
# Run inference
sentences = [
'Câu trả lời đơn giản là có, chồi hoa trên rau diếp là một dấu hiệu chắc chắn của việc bắt vít.',
'Có vẻ như nó đã bắt đầu bắt đầu.',
'Hai người đàn ông đang đợi một chuyến đi bên lề đường đất.',
]
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
- Dataset:
sts-evaluator
- Evaluated with
EmbeddingSimilarityEvaluator
Metric | Value |
---|---|
pearson_cosine | 0.5692 |
spearman_cosine | 0.5882 |
pearson_manhattan | 0.7068 |
spearman_manhattan | 0.7122 |
pearson_euclidean | 0.4671 |
spearman_euclidean | 0.5309 |
pearson_dot | 0.3262 |
spearman_dot | 0.4802 |
pearson_max | 0.7068 |
spearman_max | 0.7122 |
Training Details
Training Hyperparameters
Non-Default Hyperparameters
overwrite_output_dir
: Trueeval_strategy
: stepsper_device_train_batch_size
: 128per_device_eval_batch_size
: 128gradient_accumulation_steps
: 2learning_rate
: 1e-05num_train_epochs
: 15lr_scheduler_type
: cosine_with_restartswarmup_ratio
: 0.1fp16
: Trueload_best_model_at_end
: Truegradient_checkpointing
: Truebatch_sampler
: no_duplicates
All Hyperparameters
Click to expand
overwrite_output_dir
: Truedo_predict
: Falseeval_strategy
: stepsprediction_loss_only
: Trueper_device_train_batch_size
: 128per_device_eval_batch_size
: 128per_gpu_train_batch_size
: Noneper_gpu_eval_batch_size
: Nonegradient_accumulation_steps
: 2eval_accumulation_steps
: Nonelearning_rate
: 1e-05weight_decay
: 0.0adam_beta1
: 0.9adam_beta2
: 0.999adam_epsilon
: 1e-08max_grad_norm
: 1.0num_train_epochs
: 15max_steps
: -1lr_scheduler_type
: cosine_with_restartslr_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
: Trueignore_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
: Truegradient_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
: Falsebatch_sampler
: no_duplicatesmulti_dataset_batch_sampler
: proportional
Training Logs
Click to expand
Epoch | Step | Training Loss | stage1 loss | stage2 loss | stage3 loss | sts-evaluator_spearman_max |
---|---|---|---|---|---|---|
0 | 0 | - | - | - | - | 0.6643 |
0.0877 | 100 | 4.3054 | - | - | - | - |
0.1754 | 200 | 3.93 | - | - | - | - |
0.2632 | 300 | 3.585 | - | - | - | - |
0.3509 | 400 | 3.4482 | - | - | - | - |
0.4386 | 500 | 3.1858 | 4.3297 | 2.6006 | 0.1494 | 0.7527 |
0.5263 | 600 | 3.141 | - | - | - | - |
0.6140 | 700 | 2.9477 | - | - | - | - |
0.7018 | 800 | 2.6271 | - | - | - | - |
0.7895 | 900 | 2.6175 | - | - | - | - |
0.8772 | 1000 | 2.4931 | 2.9001 | 2.3487 | 0.1593 | 0.6907 |
0.9649 | 1100 | 2.4516 | - | - | - | - |
1.0526 | 1200 | 2.4662 | - | - | - | - |
1.1404 | 1300 | 2.5022 | - | - | - | - |
1.2281 | 1400 | 2.4325 | - | - | - | - |
1.3158 | 1500 | 2.4058 | 2.7163 | 2.1658 | 0.1392 | 0.7121 |
1.4035 | 1600 | 2.3305 | - | - | - | - |
1.4912 | 1700 | 2.2677 | - | - | - | - |
1.5789 | 1800 | 2.2555 | - | - | - | - |
1.6667 | 1900 | 2.2275 | - | - | - | - |
1.7544 | 2000 | 2.1846 | 2.5441 | 2.1172 | 0.1293 | 0.6781 |
1.8421 | 2100 | 2.2007 | - | - | - | - |
1.9298 | 2200 | 2.192 | - | - | - | - |
2.0175 | 2300 | 2.1491 | - | - | - | - |
2.1053 | 2400 | 2.2419 | - | - | - | - |
2.1930 | 2500 | 2.1822 | 2.4765 | 2.0476 | 0.1055 | 0.6893 |
2.2807 | 2600 | 2.1384 | - | - | - | - |
2.3684 | 2700 | 2.1379 | - | - | - | - |
2.4561 | 2800 | 2.0558 | - | - | - | - |
2.5439 | 2900 | 2.057 | - | - | - | - |
2.6316 | 3000 | 2.0263 | 2.4108 | 2.0751 | 0.0904 | 0.7016 |
2.7193 | 3100 | 1.9587 | - | - | - | - |
2.8070 | 3200 | 2.0702 | - | - | - | - |
2.8947 | 3300 | 2.0058 | - | - | - | - |
2.9825 | 3400 | 2.0093 | - | - | - | - |
3.0702 | 3500 | 2.0347 | 2.3948 | 1.9958 | 0.0937 | 0.7131 |
3.1579 | 3600 | 2.0071 | - | - | - | - |
3.2456 | 3700 | 1.9708 | - | - | - | - |
3.3333 | 3800 | 2.027 | - | - | - | - |
3.4211 | 3900 | 1.9432 | - | - | - | - |
3.5088 | 4000 | 1.9245 | 2.3858 | 2.0274 | 0.0831 | 0.7197 |
3.5965 | 4100 | 1.8814 | - | - | - | - |
3.6842 | 4200 | 1.8619 | - | - | - | - |
3.7719 | 4300 | 1.8987 | - | - | - | - |
3.8596 | 4400 | 1.8764 | - | - | - | - |
3.9474 | 4500 | 1.8908 | 2.3753 | 2.0066 | 0.0872 | 0.7052 |
4.0351 | 4600 | 1.8737 | - | - | - | - |
4.1228 | 4700 | 1.9289 | - | - | - | - |
4.2105 | 4800 | 1.8755 | - | - | - | - |
4.2982 | 4900 | 1.8542 | - | - | - | - |
4.3860 | 5000 | 1.8514 | 2.3731 | 2.0023 | 0.0824 | 0.7191 |
4.4737 | 5100 | 1.7939 | - | - | - | - |
4.5614 | 5200 | 1.8126 | - | - | - | - |
4.6491 | 5300 | 1.7662 | - | - | - | - |
4.7368 | 5400 | 1.7448 | - | - | - | - |
4.8246 | 5500 | 1.7736 | 2.3703 | 2.0038 | 0.0768 | 0.7044 |
4.9123 | 5600 | 1.7993 | - | - | - | - |
5.0 | 5700 | 1.7811 | - | - | - | - |
5.0877 | 5800 | 1.7905 | - | - | - | - |
5.1754 | 5900 | 1.7539 | - | - | - | - |
5.2632 | 6000 | 1.7393 | 2.3568 | 2.0173 | 0.0853 | 0.7263 |
5.3509 | 6100 | 1.7882 | - | - | - | - |
5.4386 | 6200 | 1.682 | - | - | - | - |
5.5263 | 6300 | 1.7175 | - | - | - | - |
5.6140 | 6400 | 1.6806 | - | - | - | - |
5.7018 | 6500 | 1.6243 | 2.3715 | 2.0202 | 0.0770 | 0.7085 |
5.7895 | 6600 | 1.7079 | - | - | - | - |
5.8772 | 6700 | 1.6743 | - | - | - | - |
5.9649 | 6800 | 1.6897 | - | - | - | - |
6.0526 | 6900 | 1.668 | - | - | - | - |
6.1404 | 7000 | 1.6806 | 2.3826 | 1.9925 | 0.0943 | 0.7072 |
6.2281 | 7100 | 1.6394 | - | - | - | - |
6.3158 | 7200 | 1.6738 | - | - | - | - |
6.4035 | 7300 | 1.6382 | - | - | - | - |
6.4912 | 7400 | 1.6109 | - | - | - | - |
6.5789 | 7500 | 1.5864 | 2.3849 | 2.0064 | 0.0831 | 0.7200 |
6.6667 | 7600 | 1.5838 | - | - | - | - |
6.7544 | 7700 | 1.5776 | - | - | - | - |
6.8421 | 7800 | 1.5904 | - | - | - | - |
6.9298 | 7900 | 1.6198 | - | - | - | - |
7.0175 | 8000 | 1.5661 | 2.3917 | 2.0038 | 0.0746 | 0.7131 |
7.1053 | 8100 | 1.6253 | - | - | - | - |
7.1930 | 8200 | 1.5564 | - | - | - | - |
7.2807 | 8300 | 1.5947 | - | - | - | - |
7.3684 | 8400 | 1.5982 | - | - | - | - |
7.4561 | 8500 | 1.53 | 2.3761 | 2.0162 | 0.0775 | 0.7189 |
7.5439 | 8600 | 1.5412 | - | - | - | - |
7.6316 | 8700 | 1.5287 | - | - | - | - |
7.7193 | 8800 | 1.4652 | - | - | - | - |
7.8070 | 8900 | 1.5611 | - | - | - | - |
7.8947 | 9000 | 1.5258 | 2.3870 | 1.9896 | 0.0828 | 0.7126 |
7.9825 | 9100 | 1.552 | - | - | - | - |
8.0702 | 9200 | 1.5287 | - | - | - | - |
8.1579 | 9300 | 1.4889 | - | - | - | - |
8.2456 | 9400 | 1.4893 | - | - | - | - |
8.3333 | 9500 | 1.5538 | 2.3810 | 1.9956 | 0.0772 | 0.7181 |
8.4211 | 9600 | 1.4863 | - | - | - | - |
8.5088 | 9700 | 1.4894 | - | - | - | - |
8.5965 | 9800 | 1.4516 | - | - | - | - |
8.6842 | 9900 | 1.4399 | - | - | - | - |
8.7719 | 10000 | 1.4699 | 2.3991 | 1.9760 | 0.0894 | 0.7122 |
Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.0.1
- Transformers: 4.42.4
- PyTorch: 2.3.1+cu121
- Accelerate: 0.33.0
- Datasets: 2.20.0
- 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",
}
TripletLoss
@misc{hermans2017defense,
title={In Defense of the Triplet Loss for Person Re-Identification},
author={Alexander Hermans and Lucas Beyer and Bastian Leibe},
year={2017},
eprint={1703.07737},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
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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Model tree for huudan123/stag_123_cp10000
Base model
vinai/phobert-base-v2
Finetuned
this model
Evaluation results
- Pearson Cosine on sts evaluatorself-reported0.569
- Spearman Cosine on sts evaluatorself-reported0.588
- Pearson Manhattan on sts evaluatorself-reported0.707
- Spearman Manhattan on sts evaluatorself-reported0.712
- Pearson Euclidean on sts evaluatorself-reported0.467
- Spearman Euclidean on sts evaluatorself-reported0.531
- Pearson Dot on sts evaluatorself-reported0.326
- Spearman Dot on sts evaluatorself-reported0.480
- Pearson Max on sts evaluatorself-reported0.707
- Spearman Max on sts evaluatorself-reported0.712