This is a sentence-transformers model finetuned from huudan123/model_stage2_latest. 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: huudan123/model_stage2_latest
Maximum Sequence Length: 256 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': 256, '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.

python

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("huudan123/model_stage3_latest")
# Run inference
sentences = [
    'Tôi có thể nghĩ ra ba yếu tố chính là những phỏng đoán khá logic.',
    'Đã có khá nhiều nghiên cứu trong bóng đá / bóng đá thảo luận về lợi thế sân nhà.',
    'Cô gái đang đứng trước cánh cửa mở của xe buýt.',
]
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	0.8455
spearman_cosine	0.8455
pearson_manhattan	0.8362
spearman_manhattan	0.8436
pearson_euclidean	0.836
spearman_euclidean	0.8435
pearson_dot	0.8302
spearman_dot	0.8289
pearson_max	0.8455
spearman_max	0.8455

Training Details

Training Hyperparameters

Non-Default Hyperparameters

overwrite_output_dir: True
eval_strategy: epoch
per_device_train_batch_size: 128
per_device_eval_batch_size: 128
learning_rate: 3e-05
weight_decay: 0.01
num_train_epochs: 15
warmup_ratio: 0.1
fp16: True
load_best_model_at_end: True
gradient_checkpointing: True

All Hyperparameters

Click to expand

overwrite_output_dir: True
do_predict: False
eval_strategy: epoch
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: 1
eval_accumulation_steps: None
torch_empty_cache_steps: None
learning_rate: 3e-05
weight_decay: 0.01
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: 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: 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
eval_use_gather_object: False
batch_sampler: batch_sampler
multi_dataset_batch_sampler: proportional

Training Logs

Epoch	Step	Training Loss	loss	sts-evaluator_spearman_max
0	0	-	-	0.6849
0.5556	25	0.0801	-	-
1.0	45	-	0.0390	0.7990
1.1111	50	0.0388	-	-
1.6667	75	0.0309	-	-
2.0	90	-	0.0315	0.8401
2.2222	100	0.0264	-	-
2.7778	125	0.0222	-	-
3.0	135	-	0.0302	0.8412
3.3333	150	0.0188	-	-
3.8889	175	0.0164	-	-
4.0	180	-	0.0300	0.8411
4.4444	200	0.0138	-	-
5.0	225	0.0135	0.0291	0.8446
5.5556	250	0.011	-	-
6.0	270	-	0.0291	0.8458
6.1111	275	0.0104	-	-
6.6667	300	0.0093	-	-
7.0	315	-	0.0280	0.8479
7.2222	325	0.0088	-	-
7.7778	350	0.0081	-	-
8.0	360	-	0.0285	0.848
8.3333	375	0.0075	-	-
8.8889	400	0.0071	-	-
9.0	405	-	0.0285	0.8463
9.4444	425	0.0066	-	-
10.0	450	0.0066	0.0287	0.8455
10.5556	475	0.0062	-	-
11.0	495	-	0.0285	0.8458
11.1111	500	0.0058	-	-
11.6667	525	0.0056	-	-
12.0	540	-	0.0291	0.8452
12.2222	550	0.0055	-	-
12.7778	575	0.0053	-	-
13.0	585	-	0.0289	0.8455

The bold row denotes the saved checkpoint.

Framework Versions

Python: 3.10.12
Sentence Transformers: 3.0.1
Transformers: 4.44.0
PyTorch: 2.4.0+cu121
Accelerate: 0.33.0
Datasets: 2.21.0
Tokenizers: 0.19.1

Citation

BibTeX

Sentence Transformers

bibtex

@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",
}

tranhuudan-fullstack-ai-engineer/model_stage3_latest

SentenceTransformer based on huudan123/model_stage2_latest