This is a sentence-transformers model finetuned from pkqinys/shawhin-clip-title-thumbnail-embeddings on the shawhin-yt-title-thumbnail-pairs dataset. 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: pkqinys/shawhin-clip-title-thumbnail-embeddings
Maximum Sequence Length: None tokens
Output Dimensionality: None dimensions
Similarity Function: Cosine Similarity
Training Dataset:
- shawhin-yt-title-thumbnail-pairs

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("sentence_transformers_model_id")
# Run inference
sentences = [
    'The Hugging Face Transformers Library | Example Code + Chatbot UI with Gradio',
    'How to Evaluate (and Improve) Your LLM Apps',
    'How to Improve LLMs with Tools (ft. OpenAI Agents SDK)',
]
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: yt-title-thumbnail-train and yt-title-thumbnail-valid
Evaluated with TripletEvaluator

Metric	yt-title-thumbnail-train	yt-title-thumbnail-valid
cosine_accuracy	1.0	0.9375

Training Details

Training Dataset

shawhin-yt-title-thumbnail-pairs

Dataset: shawhin-yt-title-thumbnail-pairs at 24578b7
Size: 75 training samples
Columns: anchor, positive, and negative
Approximate statistics based on the first 75 samples:
anchor positive negative
type PIL.JpegImagePlugin.JpegImageFile string string
details
min: 8 tokens
mean: 15.52 tokens
max: 27 tokens
min: 8 tokens
mean: 15.49 tokens
max: 27 tokens

Samples:

anchor	positive	negative
`<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=480x360 at 0x350EB2A90>`	`A Practical Introduction to Large Language Models (LLMs)`	`Prompt Engineering: How to Trick AI into Solving Your Problems`
`<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=480x360 at 0x350EB3810>`	`How to Build a Notion AI Agent (in 18 minutes)`	`4 Ways to Measure Fat Tails with Python (+ Example Code)`
`<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=480x360 at 0x350EB3650>`	`Context Engineering Explained (5 Practical Tips)`	`Why I Quit My $150,000 Data Science Job`

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

Evaluation Dataset

shawhin-yt-title-thumbnail-pairs

Dataset: shawhin-yt-title-thumbnail-pairs at 24578b7
Size: 16 evaluation samples
Columns: anchor, positive, and negative
Approximate statistics based on the first 16 samples:
anchor positive negative
type PIL.JpegImagePlugin.JpegImageFile string string
details
min: 8 tokens
mean: 14.5 tokens
max: 22 tokens
min: 9 tokens
mean: 13.56 tokens
max: 18 tokens

Samples:

anchor	positive	negative
`<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=480x360 at 0x3AEE5D850>`	`Principal Component Analysis (PCA)`	Introduction & Example (Python) Code
`<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=480x360 at 0x3AEE5EA10>`	`5 Reasons Why Every Data Scientist Should Consider Freelancing`	`Text Embeddings, Classification, and Semantic Search (w/ Python Code)`
`<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=480x360 at 0x3AEE5DF50>`	`5 AI Projects You Can Build This Weekend (with Python)`	`The Wavelet Transform`

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: 4
per_device_eval_batch_size: 4
learning_rate: 1e-05
num_train_epochs: 4

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: epoch
prediction_loss_only: True
per_device_train_batch_size: 4
per_device_eval_batch_size: 4
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: 1e-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: 4
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

Training Logs

Click to expand

Epoch	Step	Training Loss	Validation Loss	yt-title-thumbnail-train_cosine_accuracy	yt-title-thumbnail-valid_cosine_accuracy
0	0	-	-	1.0	0.9375
0.2	1	0.5678	-	-	-
0.4	2	0.8427	-	-	-
0.6	3	0.8475	-	-	-
0.8	4	0.9326	-	-	-
1.0	5	0.9615	1.7712	1.0	0.9375
0.1	1	0.1598	-	-	-
0.2	2	0.084	-	-	-
0.3	3	0.2701	-	-	-
0.4	4	0.4036	-	-	-
0.5	5	0.3907	-	-	-
0.6	6	0.4165	-	-	-
0.7	7	0.6304	-	-	-
0.8	8	0.3674	-	-	-
0.9	9	0.7016	-	-	-
1.0	10	0.2821	1.2305	-	-
1.1	11	0.2749	-	-	-
1.2	12	0.4927	-	-	-
1.3	13	0.2579	-	-	-
1.4	14	0.5595	-	-	-
1.5	15	0.5023	-	-	-
1.6	16	0.2853	-	-	-
1.7	17	0.44	-	-	-
1.8	18	0.5634	-	-	-
1.9	19	0.4209	-	-	-
2.0	20	0.0825	1.2300	-	-
0.0526	1	0.0705	-	-	-
0.1053	2	0.0566	-	-	-
0.1579	3	0.0493	-	-	-
0.2105	4	0.0315	-	-	-
0.2632	5	0.0866	-	-	-
0.3158	6	0.069	-	-	-
0.3684	7	0.3537	-	-	-
0.4211	8	0.122	-	-	-
0.4737	9	0.1622	-	-	-
0.5263	10	0.1299	-	-	-
0.5789	11	0.2733	-	-	-
0.6316	12	0.1462	-	-	-
0.6842	13	0.3868	-	-	-
0.7368	14	0.2076	-	-	-
0.7895	15	0.1524	-	-	-
0.8421	16	0.2408	-	-	-
0.8947	17	0.3624	-	-	-
0.9474	18	0.3834	-	-	-
1.0	19	0.2456	0.7823	-	-
1.0526	20	0.1087	-	-	-
1.1053	21	0.1406	-	-	-
1.1579	22	0.1029	-	-	-
1.2105	23	0.1475	-	-	-
1.2632	24	0.1049	-	-	-
1.3158	25	0.053	-	-	-
1.3684	26	0.3995	-	-	-
1.4211	27	0.1227	-	-	-
1.4737	28	0.2695	-	-	-
1.5263	29	0.2224	-	-	-
1.5789	30	0.0945	-	-	-
1.6316	31	0.1475	-	-	-
1.6842	32	0.4774	-	-	-
1.7368	33	0.0315	-	-	-
1.7895	34	0.1688	-	-	-
1.8421	35	0.3641	-	-	-
1.8947	36	0.1004	-	-	-
1.9474	37	0.1414	-	-	-
2.0	38	0.067	0.7837	-	-
2.0526	39	0.1435	-	-	-
2.1053	40	0.2146	-	-	-
2.1579	41	0.2392	-	-	-
2.2105	42	0.1392	-	-	-
2.2632	43	0.3378	-	-	-
2.3158	44	0.0715	-	-	-
2.3684	45	0.1154	-	-	-
2.4211	46	0.2249	-	-	-
2.4737	47	0.0407	-	-	-
2.5263	48	0.0414	-	-	-
2.5789	49	0.1295	-	-	-
2.6316	50	0.0922	-	-	-
2.6842	51	0.077	-	-	-
2.7368	52	0.4554	-	-	-
2.7895	53	0.0699	-	-	-
2.8421	54	0.0663	-	-	-
2.8947	55	0.2612	-	-	-
2.9474	56	0.1907	-	-	-
3.0	57	0.1049	0.7811	-	-
3.0526	58	0.1027	-	-	-
3.1053	59	0.2408	-	-	-
3.1579	60	0.0248	-	-	-
3.2105	61	0.2142	-	-	-
3.2632	62	0.1579	-	-	-
3.3158	63	0.0789	-	-	-
3.3684	64	0.0668	-	-	-
3.4211	65	0.1484	-	-	-
3.4737	66	0.3956	-	-	-
3.5263	67	0.1063	-	-	-
3.5789	68	0.4022	-	-	-
3.6316	69	0.5607	-	-	-
3.6842	70	0.0283	-	-	-
3.7368	71	0.0781	-	-	-
3.7895	72	0.248	-	-	-
3.8421	73	0.08	-	-	-
3.8947	74	0.2495	-	-	-
3.9474	75	0.1528	-	-	-
4.0	76	0.2234	-	-	-

Framework Versions

Python: 3.11.13
Sentence Transformers: 3.3.1
Transformers: 4.48.0
PyTorch: 2.3.0
Accelerate: 1.10.1
Datasets: 4.1.1
Tokenizers: 0.21.4

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.JpegImagePlugin.JpegImageFile	string	string
details		min: 8 tokens mean: 15.52 tokens max: 27 tokens	min: 8 tokens mean: 15.49 tokens max: 27 tokens