This is a sentence-transformers model finetuned from shibing624/text2vec-base-multilingual. It maps sentences & paragraphs to a 384-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: shibing624/text2vec-base-multilingual
Maximum Sequence Length: 512 tokens
Output Dimensionality: 384 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: BertModel 
  (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, '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("T-Blue/tsdae_pro_text2vec")
# Run inference
sentences = [
    '𑀠चपच𑀞𑀢𑀟 पच𑀟च ढनबच 𑀱च 𑀟च𑀠ध𑁣ल लच𑀣𑀢𑁦𑀳 𑀲त पच 𑀱च𑀳च𑀯',
    ' च 𑀠चपच𑀞𑀢𑀟 𑀞नल𑁣ढ पच𑀟च ढनबच 𑀱च 𑀞𑁣𑀠च𑀳 𑀟च𑀠ध𑁣ल लच𑀣𑀢𑁦𑀳 𑀲त पच 𑀟च𑀠𑀢ढ𑀢च 𑀱च𑀳च𑀯',
    ' णच𑀟𑀞न𑀟च𑀟 बन𑀟𑀣न𑀠च𑀪 𑀘𑀣𑁦ण𑀣𑁦𑀫 ब𑀢𑀣च 𑀟𑁦 बच ब𑀢𑀣च𑀘𑁦 𑀠च𑀳न णच𑀱च 𑀟च झच𑀪𑀟𑀢 𑀟च 𑀭𑁢 𑀣च 𑀟च 𑀭𑀬 𑀟च चल𑁦धध𑀢𑀟 ढ𑁣न𑀪ब𑁦𑁣𑀢𑀳𑀢𑁦𑀦 𑀱चञच𑀟𑀣च 𑀞𑁦 ञचन𑀞𑁦 𑀣च 𑀤च𑀟𑁦𑀟 𑀣नप𑀳𑁦𑀯',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]

Training Details

Training Dataset

Unnamed Dataset

Size: 64,000 training samples
Columns: sentence_0 and sentence_1
Approximate statistics based on the first 1000 samples:
sentence_0 sentence_1
type string string
details
min: 3 tokens
mean: 37.42 tokens
max: 342 tokens
min: 4 tokens
mean: 89.84 tokens
max: 512 tokens

Samples:

sentence_0	sentence_1
`𑀠नपच𑀟𑁦𑀫च𑀢𑀫न𑀱च𑀟 𑀭थथ𑀬𑀯`	`𑀞𑀢𑀣𑀢𑀣𑀣𑀢बच𑀪 𑀳च𑀟च𑀙च𑀞नल𑁣ढझच𑀳च𑀳𑀫𑁦𑀟 𑀣न𑀟𑀢णच𑀠च𑀟च𑀤च𑀪पच 𑀪चणचणणन𑀟 𑀠नपच𑀟𑁦𑀫च𑀢𑀫न𑀱च𑀟 𑀭थथ𑀬𑀯`
`च 𑀱च𑀘𑁦𑀟 𑀘च𑀠भ𑀢णणच 𑀠च𑀢 𑀞𑀢𑀳𑀫𑀢𑀟 पच बच𑀳𑀞𑀢णच𑀯`	`𑀘च𑀠भ𑀢णणच𑀪 च ल𑁣𑀞चत𑀢𑀟 𑀢पच त𑁦 पच ढ𑀢णन 𑀣च पच ण𑀢 𑀟च𑀠𑀢𑀘𑀢𑀟 𑀞𑁣𑀞च𑀪𑀢 𑀱च𑀘𑁦𑀟 𑀳च𑀠च𑀪 𑀣च 𑀘च𑀠भ𑀢णणच 𑀠च𑀢 𑀞𑀢𑀳𑀫𑀢𑀟 𑀞च𑀳च पच बच𑀳𑀞𑀢णच𑀯`
`𑀯`	`𑀯`

Loss: DenoisingAutoEncoderLoss

Training Hyperparameters

Non-Default Hyperparameters

per_device_train_batch_size: 16
per_device_eval_batch_size: 16
multi_dataset_batch_sampler: round_robin

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: no
prediction_loss_only: True
per_device_train_batch_size: 16
per_device_eval_batch_size: 16
per_gpu_train_batch_size: None
per_gpu_eval_batch_size: None
gradient_accumulation_steps: 1
eval_accumulation_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: 3
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: False
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
batch_sampler: batch_sampler
multi_dataset_batch_sampler: round_robin

Training Logs

Epoch	Step	Training Loss
0.125	500	4.0592
0.25	1000	1.6454
0.375	1500	1.4774
0.5	2000	1.4131
0.625	2500	1.3766
0.75	3000	1.3488
0.875	3500	1.3252
1.0	4000	1.3087
1.125	4500	1.2931
1.25	5000	1.2772
1.375	5500	1.2655
1.5	6000	1.2535
1.625	6500	1.243
1.75	7000	1.2305
1.875	7500	1.223
2.0	8000	1.216
2.125	8500	1.2073
2.25	9000	1.1999
2.375	9500	1.1935
2.5	10000	1.1872
2.625	10500	1.1804
2.75	11000	1.17
2.875	11500	1.167
3.0	12000	1.1623

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: 2.18.0
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",
}

DenoisingAutoEncoderLoss

bibtex

@inproceedings{wang-2021-TSDAE,
    title = "TSDAE: Using Transformer-based Sequential Denoising Auto-Encoderfor Unsupervised Sentence Embedding Learning",
    author = "Wang, Kexin and Reimers, Nils and Gurevych, Iryna", 
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2021",
    month = nov,
    year = "2021",
    address = "Punta Cana, Dominican Republic",
    publisher = "Association for Computational Linguistics",
    pages = "671--688",
    url = "https://arxiv.org/abs/2104.06979",
}

	sentence_0	sentence_1
type	string	string
details	min: 3 tokens mean: 37.42 tokens max: 342 tokens	min: 4 tokens mean: 89.84 tokens max: 512 tokens