This is a sentence-transformers model finetuned from sentence-transformers/clip-ViT-B-32. It maps sentences & paragraphs to a None-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: sentence-transformers/clip-ViT-B-32
Maximum Sequence Length: 77 tokens
Output Dimensionality: None 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): CLIPModel()
)

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("WorkStation0/clip-finetuned-satellite-v.0.11")
# Run inference
sentences = [
    "It's a mountainous area.it is white, brown and green .It's a piece of green mountains.it is a piece of green mountains .this is mountainous region .",
    'Many aircraft are parked next to the terminals near the runways of an airport.Many aircraft are parked next to terminals near airstrips at an airport.Many planes are parked next to the terminals near the runways in an airport.many planes are parked next to terminals near runways at an airport.many planes are parked next to terminals near runways in an airport .',
    'yellow ribbon beach is between green trees and dark green ocean with white waves.yellow ribbon beach lies between green trees and dark green ocean with white waves.Yellow ribbon beach is between green trees and dark green ocean with white waves.Yellow ribbon beach is between green trees and dark green ocean with white waves.yellow ribbon beach is between green trees and dark green ocean with white waves .',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

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

Evaluation

Metrics

Triplet

Datasets: clip-valid-triplet, clip-train-triplet and clip-test-triplet
Evaluated with TripletEvaluator

Metric	clip-valid-triplet	clip-train-triplet	clip-test-triplet
cosine_accuracy	0.9992	0.9997	0.9992

Training Details

Training Dataset

Unnamed Dataset

Size: 6,113 training samples
Columns: anchor, positive, and negative
Approximate statistics based on the first 1000 samples:
anchor positive negative
type PIL.PngImagePlugin.PngImageFile string string
details
min: 36 tokens
mean: 60.87 tokens
max: 77 tokens
min: 29 tokens
mean: 54.49 tokens
max: 77 tokens

Samples:

anchor	positive	negative
`<PIL.PngImagePlugin.PngImageFile image mode=RGB size=224x224 at 0x7A03B1561BD0>`	`Many buildings and some green trees are located in an industrial area.there are many parking cars on the area next to the buildings in the industrial region.there are many cars parking on the region beside the buildings in the industrial region .many buildings and some green trees are located in an industrial area.many buildings and some green trees are in an industrial area .`	`It's a piece of white snow mountain.It's a piece of snow white mountain.It's a piece of white snow mountain.It's a piece of white snow mountain.it is a piece of white snow mountain .`
`<PIL.PngImagePlugin.PngImageFile image mode=RGB size=224x224 at 0x7A03B1560390>`	`Many buildings are located in a commercial area.Many buildings are located in a commercial area.Many buildings are located in a commercial area.Many buildings are in a commercial area.many buildings are in a commercial area .`	`The mountain of yellow and green has a vein texture.the mountain of yellow and green has a vein texture.the mountain of yellow and green has a texture of vein .mountains with long and narrow ridges traverse in this range .it is a piece of irregular green mountains .`
`<PIL.PngImagePlugin.PngImageFile image mode=RGB size=224x224 at 0x7A03B1560C50>`	`There's a strong bridge over the river.There are many houses on both sides of the river.there are many houses on both sides of the river .There's a strong bridge over the river.there is a strong bridge over the river .`	`many small green spots are scattered in a piece of kaki nueland.Many small green spots are scattered in a piece of naked khaki.Many small green spots are scattered in a piece of khaki stripe.many small green spots are scattered in a piece of khaki bareland.many small green spots are scattered in a piece of khaki bareland .`

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

Evaluation Dataset

Unnamed Dataset

Size: 1,310 evaluation samples
Columns: anchor, positive, and negative
Approximate statistics based on the first 1000 samples:
anchor positive negative
type PIL.PngImagePlugin.PngImageFile string string
details
min: 33 tokens
mean: 60.71 tokens
max: 77 tokens
min: 29 tokens
mean: 55.17 tokens
max: 77 tokens

Samples:

anchor	positive	negative
`<PIL.PngImagePlugin.PngImageFile image mode=RGB size=224x224 at 0x7A03B2DEDC50>`	`Many trees are planted around the playground.There's a green football pitch on the red track.there's a green football field on the red track .a lot of trees are planted around the playground.a lot of trees are planted around the playground .`	`It's a piece of uneven kaki nueland.It's a piece of uneven naked khaki.It's a piece of irregular Kaki stripping.It's a piece of unequally brazen khaki.it is a piece of uneven khaki bareland .`
`<PIL.PngImagePlugin.PngImageFile image mode=RGB size=224x224 at 0x7A03B2C5E010>`	`It's a big bridge and a few buildings with some grass.the roads are grey and the ground is brown .two parallel bridges on a black river are close to many green plants and several buildings.two parallel bridges on a black river are near many green plants and several buildings .this is a big bridge and some buildings with a little grass .`	`Cylinder storage tanks are built on two square concrete fields near some trees and a parking lot.Cylinder storage tanks are built on two square concrete grounds near some trees and a parking lot.cylinder storage tanks are built on two square concrete ground near some trees and a parking lot .these storage tanks are painted in different colors located next to the wood .many storage tanks are near some green trees .`
`<PIL.PngImagePlugin.PngImageFile image mode=RGB size=224x224 at 0x7A03B2DB1250>`	`the lake with borders is surrounded by roads a parking lot and rows of houses.the lake with edges is surrounded by roads a parking lot and rows of houses.the lake with bylands is surrounded by roads a parking lot and rows of houses .green ponds sit in this resort surrounded by rows of red houses .some buildings and green trees are in a resort with several green ponds .`	`many storage tanks of different sizes are in a factory.Many storage tanks in different sizes are in a factory.Many storage tanks in different sizes are in a factory.many storage tanks in different sizes are in a factory.many storage tanks in different sizes are in a factory .`

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

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: epoch
per_device_train_batch_size: 2
per_device_eval_batch_size: 2
gradient_accumulation_steps: 4
fp16: True
dataloader_num_workers: 2
load_best_model_at_end: True

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: epoch
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: 4
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
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: 2
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: 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
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

Training Logs

Epoch	Step	Training Loss	Validation Loss	clip-valid-triplet_cosine_accuracy	clip-train-triplet_cosine_accuracy	clip-test-triplet_cosine_accuracy
-1	-1	-	-	0.9863	-	-
0.0994	76	1.3892	-	-	-	-
0.1989	152	0.7274	-	-	-	-
0.2983	228	0.5566	-	-	-	-
0.3978	304	0.4034	-	-	-	-
0.4972	380	0.4335	-	-	-	-
0.5967	456	0.4176	-	-	-	-
0.6961	532	0.3955	-	-	-	-
0.7956	608	0.3396	-	-	-	-
0.8950	684	0.306	-	-	-	-
0.9944	760	0.2526	-	-	-	-
1.0	765	-	0.0786	1.0	-	-
1.0929	836	0.2531	-	-	-	-
1.1923	912	0.3303	-	-	-	-
1.2918	988	0.2122	-	-	-	-
1.3912	1064	0.2478	-	-	-	-
1.4907	1140	0.3588	-	-	-	-
1.5901	1216	0.2023	-	-	-	-
1.6896	1292	0.2395	-	-	-	-
1.7890	1368	0.1154	-	-	-	-
1.8885	1444	0.2729	-	-	-	-
1.9879	1520	0.1708	-	-	-	-
2.0	1530	-	0.0700	0.9992	-	-
2.0864	1596	0.1841	-	-	-	-
2.1858	1672	0.1382	-	-	-	-
2.2852	1748	0.1452	-	-	-	-
2.3847	1824	0.2616	-	-	-	-
2.4841	1900	0.2024	-	-	-	-
2.5836	1976	0.2883	-	-	-	-
2.6830	2052	0.2461	-	-	-	-
2.7825	2128	0.3249	-	-	-	-
2.8819	2204	0.1299	-	-	-	-
2.9814	2280	0.2171	-	-	-	-
3.0	2295	-	0.0652	0.9992	-	-
-1	-1	-	-	0.9992	0.9997	0.9992

Framework Versions

Python: 3.11.12
Sentence Transformers: 4.1.0
Transformers: 4.52.3
PyTorch: 2.6.0+cu124
Accelerate: 1.7.0
Datasets: 2.14.4
Tokenizers: 0.21.1

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",
}

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}
}

	anchor	positive	negative
type	PIL.PngImagePlugin.PngImageFile	string	string
details		min: 36 tokens mean: 60.87 tokens max: 77 tokens	min: 29 tokens mean: 54.49 tokens max: 77 tokens