metadata

tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - dense
  - generated_from_trainer
  - dataset_size:70
  - loss:MultipleNegativesRankingLoss
base_model: sentence-transformers/clip-ViT-L-14
widget:
  - source_sentence: How to Manage Data Science Projects
    sentences:
      - Fine-Tuning Text Embeddings For Domain-specific Search (w/ Python)
      - I Was Wrong About AI Consulting (what I learned)
      - What Nature Can Teach Us About Business...
  - source_sentence: 4 Ways to Measure Fat Tails with Python (+ Example Code)
    sentences:
      - How I’d Learn AI in 2025 (if I could start over)
      - A Practical Introduction to Large Language Models (LLMs)
      - Fine-tuning Large Language Models (LLMs) | w/ Example Code
  - source_sentence: Dimensionality Reduction & Segmentation with Decision Trees | Python Code
    sentences:
      - 5 AI Projects For People in a Hurry (w/ Python)
      - How to Improve LLMs with RAG (Overview + Python Code)
      - How to Build an LLM from Scratch | An Overview
  - source_sentence: What Is Data Science & How To Start? | A Beginner's Guide
    sentences:
      - 3 AI Use Cases (that are not a chatbot)
      - The OpenAI (Python) API | Introduction & Example Code
      - Time Series, Signals, & the Fourier Transform | Introduction
  - source_sentence: 5 Questions Every Data Scientist Should Hardcode into Their Brain
    sentences:
      - How to Improve LLMs with Tools (ft. OpenAI Agents SDK)
      - ML Foundations for AI Engineers (in 34 Minutes)
      - 'Causality: An Introduction | How (naive) statistics can fail us'
datasets:
  - prashgec/my-learning-ds
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
  - cosine_accuracy
model-index:
  - name: SentenceTransformer based on sentence-transformers/clip-ViT-L-14
    results:
      - task:
          type: triplet
          name: Triplet
        dataset:
          name: yt title thumbnail train
          type: yt-title-thumbnail-train
        metrics:
          - type: cosine_accuracy
            value: 1
            name: Cosine Accuracy
      - task:
          type: triplet
          name: Triplet
        dataset:
          name: yt title thumbnail valid
          type: yt-title-thumbnail-valid
        metrics:
          - type: cosine_accuracy
            value: 0.8666666746139526
            name: Cosine Accuracy

SentenceTransformer based on sentence-transformers/clip-ViT-L-14

This is a sentence-transformers model finetuned from sentence-transformers/clip-ViT-L-14 on the my-learning-ds 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: sentence-transformers/clip-ViT-L-14
Maximum Sequence Length: 77 tokens
Output Dimensionality: None dimensions
Similarity Function: Cosine Similarity
Training Dataset:
- my-learning-ds

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.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("prashgec/clip-title-thumbnail-embeddings")
# Run inference
sentences = [
    '5 Questions Every Data Scientist Should Hardcode into Their Brain',
    'How to Improve LLMs with Tools (ft. OpenAI Agents SDK)',
    'ML Foundations for AI Engineers (in 34 Minutes)',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.6706, 0.7328],
#         [0.6706, 1.0000, 0.8154],
#         [0.7328, 0.8154, 1.0000]])

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	0.8667

Training Details

Training Dataset

my-learning-ds

Dataset: my-learning-ds at 70c7274
Size: 70 training samples
Columns: anchor, positive, and negative
Approximate statistics based on the first 70 samples:
anchor positive negative
type PIL.JpegImagePlugin.JpegImageFile string string
details

min: 8 tokens
mean: 15.13 tokens
max: 27 tokens

min: 8 tokens
mean: 15.34 tokens
max: 27 tokens

	anchor	positive	negative
type	PIL.JpegImagePlugin.JpegImageFile	string	string
details		min: 8 tokens mean: 15.13 tokens max: 27 tokens	min: 8 tokens mean: 15.34 tokens max: 27 tokens

Samples:

anchor	positive	negative
	`Causal Effects`	An introduction
	`3 Ways to Make a Custom AI Assistant`	RAG, Tools, & Fine-tuning
	`Prompt Engineering: How to Trick AI into Solving Your Problems`	`Dimensionality Reduction & Segmentation with Decision Trees`

Loss: MultipleNegativesRankingLoss with these parameters:

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

Evaluation Dataset

my-learning-ds

Dataset: my-learning-ds at 70c7274
Size: 15 evaluation samples
Columns: anchor, positive, and negative
Approximate statistics based on the first 15 samples:
anchor positive negative
type PIL.JpegImagePlugin.JpegImageFile string string
details

min: 8 tokens
mean: 14.07 tokens
max: 22 tokens

min: 10 tokens
mean: 15.0 tokens
max: 21 tokens

	anchor	positive	negative
type	PIL.JpegImagePlugin.JpegImageFile	string	string
details		min: 8 tokens mean: 14.07 tokens max: 22 tokens	min: 10 tokens mean: 15.0 tokens max: 21 tokens

Samples:

anchor	positive	negative
	`The Wavelet Transform`	Introduction & Example Code
	`Smoothing Crypto Time Series with Wavelets`	Real-world Data Project
	`3 Reasons Businesses Should NOT Use AI`	`Fine-tuning Large Language Models (LLMs)`

Loss: MultipleNegativesRankingLoss with these parameters:

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

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: epoch
per_device_train_batch_size: 16
per_device_eval_batch_size: 16
learning_rate: 0.0001
num_train_epochs: 2

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: epoch
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
torch_empty_cache_steps: None
learning_rate: 0.0001
weight_decay: 0.0
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
max_grad_norm: 1.0
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: 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
hub_revision: None
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
liger_kernel_config: None
eval_use_gather_object: False
average_tokens_across_devices: False
prompts: None
batch_sampler: batch_sampler
multi_dataset_batch_sampler: proportional
router_mapping: {}
learning_rate_mapping: {}

Training Logs

Epoch	Step	Training Loss	Validation Loss	yt-title-thumbnail-train_cosine_accuracy	yt-title-thumbnail-valid_cosine_accuracy
-1	-1	-	-	0.9571	0.8000
0.2	1	2.0436	-	-	-
0.4	2	2.1845	-	-	-
0.6	3	1.9404	-	-	-
0.8	4	2.0339	-	-	-
1.0	5	0.9129	2.2639	-	-
1.2	6	1.3342	-	-	-
1.4	7	1.6938	-	-	-
1.6	8	1.6759	-	-	-
1.8	9	1.423	-	-	-
2.0	10	0.7338	2.2676	-	-
-1	-1	-	-	1.0	0.8667

Framework Versions

Python: 3.9.23
Sentence Transformers: 5.0.0
Transformers: 4.53.2
PyTorch: 2.7.1
Accelerate: 1.9.0
Datasets: 4.0.0
Tokenizers: 0.21.2

Citation

BibTeX

Sentence Transformers

@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

@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}
}