This is a sentence-transformers model finetuned from bau0221/ptz_embedding. 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: bau0221/ptz_embedding
Maximum Sequence Length: 512 tokens
Output Dimensionality: 384 dimensions
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': 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})
  (2): Normalize()
)

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("bau0221/ptz_embedding_ver3")
# Run inference
sentences = [
    'group1 put Abigail on the right side',
    'Move Charlotte to the right on Camera 4',
    'Set Evelyn at the right side on Camera 1',
]
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: 710 training samples
Columns: query, pos, and neg
Approximate statistics based on the first 710 samples:
query pos neg
type string list list
details
min: 5 tokens
mean: 11.46 tokens
max: 28 tokens
size: 3 elements
size: 3 elements

Samples:

query	pos	neg
`Set camera 1 to track target A at bottom_right with fast speed.`	`['Set camera 1 to track target A at bottom_right with fast speed.', 'Set camera 1 to track target A at bottom_right with fast speed.', 'Set camera 1 to track target A at bottom_right with fast speed.']`	`['Camera 2 tracking Kyle', 'Set camera 3 to track target B at the top with slow speed.', 'Turn camera 2 to the right for 5 seconds.']`
`Camera 2 tracking Kyle`	`['Camera 2 tracking Kyle', 'Camera 4 tracking Kyle', 'Cam 2 tracking Kyle']`	`['Set camera 1 to track target A at bottom_right with fast speed.', 'Set camera 3 to track target B at the top with slow speed.', 'Turn camera 2 to the right for 5 seconds.']`
`Set camera 3 to track target B at the top with slow speed.`	`['Set camera 3 to track target B at the top with slow speed.', 'Set camera 3 to track target B at the top with slow speed.', 'Set camera 3 to track target B at the top with slow speed.']`	`['Set camera 1 to track target A at bottom_right with fast speed.', 'Camera 2 tracking Kyle', 'Turn camera 2 to the right for 5 seconds.']`

Loss: MultipleNegativesRankingLoss with these parameters:
json
1{ 2 "scale": 20.0, 3 "similarity_fct": "cos_sim" 4}

Evaluation Dataset

Unnamed Dataset

Size: 71 evaluation samples
Columns: query, pos, and neg
Approximate statistics based on the first 71 samples:
query pos neg
type string list list
details
min: 6 tokens
mean: 10.35 tokens
max: 12 tokens
size: 3 elements
size: 3 elements

Samples:

query	pos	neg
`Camera 3 put Harper at the right side`	`['Camera 3 put Harper at the right side', 'Cam 3 put Harper at the right side', 'Camera 3 put Harper at the right side']`	`['Set camera 1 to track target A at bottom_right with fast speed.', 'Camera 2 tracking Kyle', 'Set camera 3 to track target B at the top with slow speed.']`
`Camera 4 put Amelia on the left side`	`['Camera 4 put Amelia on the left side', 'Cam 4 put Amelia on the left side', 'Camera 4 put Amelia on the left side']`	`['Set camera 1 to track target A at bottom_right with fast speed.', 'Camera 2 tracking Kyle', 'Set camera 3 to track target B at the top with slow speed.']`
`Group2 put Logan at the right side`	`['Group2 put Logan at the right side', 'Group2 put Logan at the right side', 'Group2 put Logan at the right side']`	`['Set camera 1 to track target A at bottom_right with fast speed.', 'Camera 2 tracking Kyle', 'Set camera 3 to track target B at the top with slow speed.']`

Loss: MultipleNegativesRankingLoss with these parameters:
json
1{ 2 "scale": 20.0, 3 "similarity_fct": "cos_sim" 4}

Training Hyperparameters

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: no
prediction_loss_only: True
per_device_train_batch_size: 8
per_device_eval_batch_size: 8
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: 1.0
num_train_epochs: 3.0
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: None
hub_always_push: False
gradient_checkpointing: False
gradient_checkpointing_kwargs: None
include_inputs_for_metrics: False
include_for_metrics: []
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
use_liger_kernel: False
eval_use_gather_object: False
average_tokens_across_devices: False
prompts: None
batch_sampler: batch_sampler
multi_dataset_batch_sampler: proportional

Framework Versions

Python: 3.10.12
Sentence Transformers: 3.3.1
Transformers: 4.47.1
PyTorch: 2.5.1+cu121
Accelerate: 1.2.1
Datasets: 3.2.0
Tokenizers: 0.21.0

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",
}

MultipleNegativesRankingLoss

bibtex

@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}
}

	query	pos	neg
type	string	list	list
details	min: 5 tokens mean: 11.46 tokens max: 28 tokens	size: 3 elements	size: 3 elements