tranhuudan-fullstack-ai-engineer/stag_123_cp8000

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_cp8000")
# 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	0.5848
spearman_cosine	0.6071
pearson_manhattan	0.7078
spearman_manhattan	0.7131
pearson_euclidean	0.4593
spearman_euclidean	0.5335
pearson_dot	0.3227
spearman_dot	0.5249
pearson_max	0.7078
spearman_max	0.7131

Training Details

Training Hyperparameters

Non-Default Hyperparameters

• overwrite_output_dir: True
• eval_strategy: steps
• per_device_train_batch_size: 128
• per_device_eval_batch_size: 128
• gradient_accumulation_steps: 2
• learning_rate: 1e-05
• num_train_epochs: 15
• lr_scheduler_type: cosine_with_restarts
• warmup_ratio: 0.1
• fp16: True
• load_best_model_at_end: True
• gradient_checkpointing: True
• batch_sampler: no_duplicates

All Hyperparameters

Click to expand

• overwrite_output_dir: True
• do_predict: False
• eval_strategy: steps
• prediction_loss_only: True
• per_device_train_batch_size: 128
• per_device_eval_batch_size: 128
• per_gpu_train_batch_size: None
• per_gpu_eval_batch_size: None
• gradient_accumulation_steps: 2
• eval_accumulation_steps: None
• learning_rate: 1e-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: 15
• max_steps: -1
• lr_scheduler_type: cosine_with_restarts
• 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: True
• 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}
• 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: False
• hub_always_push: False
• gradient_checkpointing: True
• gradient_checkpointing_kwargs: None
• include_inputs_for_metrics: False
• 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
• dispatch_batches: None
• split_batches: 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
• batch_sampler: no_duplicates
• multi_dataset_batch_sampler: proportional

Training Logs

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

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}
}