SentenceTransformer based on vinai/phobert-base

This is a sentence-transformers model finetuned from vinai/phobert-base. 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
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.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("phongtintruong/clphobert-base")
# Run inference
sentences = [
    'tăng thêm ánh_sáng bóng hắt tường 2 lên mức 51 phần_trăm',
    'tăng đến đèn 3 phòng hội_nghị thêm 27 phần_trăm',
    'điều_chỉnh hộ âm_thanh đa vùng mức 1',
]
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

Information Retrieval

Dataset: phobert-base_train_eval
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.0255
cosine_accuracy@3	0.051
cosine_accuracy@5	0.0842
cosine_accuracy@10	0.1173
cosine_precision@1	0.0255
cosine_precision@3	0.0196
cosine_precision@5	0.0209
cosine_precision@10	0.0209
cosine_recall@1	0.0006
cosine_recall@3	0.0015
cosine_recall@5	0.0026
cosine_recall@10	0.0052
cosine_ndcg@10	0.0212
cosine_mrr@10	0.0477
cosine_map@100	0.0127
dot_accuracy@1	0.0383
dot_accuracy@3	0.0714
dot_accuracy@5	0.102
dot_accuracy@10	0.1148
dot_precision@1	0.0383
dot_precision@3	0.0298
dot_precision@5	0.0311
dot_precision@10	0.0288
dot_recall@1	0.001
dot_recall@3	0.0022
dot_recall@5	0.0039
dot_recall@10	0.0072
dot_ndcg@10	0.0297
dot_mrr@10	0.06
dot_map@100	0.0147

Training Details

Training Dataset

Unnamed Dataset

Size: 7,160 training samples
Columns: sentence_0, sentence_1, and sentence_2

Approximate statistics based on the first 1000 samples:

	sentence_0	sentence_1	sentence_2
type	string	string	string
details	min: 5 tokens mean: 13.03 tokens max: 32 tokens	min: 5 tokens mean: 12.62 tokens max: 32 tokens	min: 4 tokens mean: 12.45 tokens max: 30 tokens

Samples:

sentence_0	sentence_1	sentence_2
`tăng bóng 3 26 phần_trăm`	`mình muốn tăng quạt lên 21 phần_trăm`	`tắt bóng hoa 2 phòng thực_hành 2`
`tăng bóng 3 26 phần_trăm`	`giúp mình tăng bóng để bàn lên 4 phần_trăm ở phòng ngủ con trai 7 nhé`	`giảm độ sáng bóng sưởi thứ 4 ấy trợ_lý ảo nó sáng quá`
`tăng bóng 3 26 phần_trăm`	`tăng lên đèn làm_việc cổng chính thêm 10 phần_trăm`	`cho mình tăng bóng kiểng thứ 2 lên 3`

Loss: MultipleNegativesRankingLoss with these parameters:

{
    "scale": 20.0,
    "similarity_fct": "cos_sim"
}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 2
per_device_eval_batch_size: 2
num_train_epochs: 2
fp16: True
multi_dataset_batch_sampler: round_robin

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: steps
prediction_loss_only: True
per_device_train_batch_size: 2
per_device_eval_batch_size: 2
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: 5e-05
weight_decay: 0.0
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
max_grad_norm: 1
num_train_epochs: 2
max_steps: -1
lr_scheduler_type: linear
lr_scheduler_kwargs: {}
warmup_ratio: 0.0
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: False
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: False
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: round_robin

Training Logs