---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:12
- loss:MultipleNegativesRankingLoss
base_model: sentence-transformers/clip-ViT-L-14
widget:
- source_sentence: 'the main power cable is connected with LPT '
sentences:
- 'the main power cable is connected with LPT '
- 'the main power cable is connected with LPT '
- /content/sample_data/images/LPT (2).jpeg
- source_sentence: 'the fuse is not blown it is working properly '
sentences:
- 'the fuse is not blown it is working properly '
- 'the fuse is not blown it is working properly '
- /content/sample_data/images/LPT (16).jpeg
- source_sentence: 'the fuse is blown and this might not work properly '
sentences:
- /content/sample_data/images/LPT (20).jpeg
- 'the fuse is blown and this might not work properly '
- 'the fuse is blown and this might not work properly '
- source_sentence: 'the fuse is blown and this might not work properly '
sentences:
- 'the fuse is blown and this might not work properly '
- /content/sample_data/images/LPT (21).jpeg
- 'the fuse is blown and this might not work properly '
- source_sentence: 'the main power cable is not connected with LPT '
sentences:
- 'the main power cable is not connected with LPT '
- /content/sample_data/images/LPT (4).jpeg
- 'the main power cable is not connected with LPT '
datasets:
- machinev/multimodalLPT2
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: 0.0
name: Cosine Accuracy
- task:
type: triplet
name: Triplet
dataset:
name: yt title thumbnail validation
type: yt-title-thumbnail-validation
metrics:
- type: cosine_accuracy
value: 0.0
name: Cosine Accuracy
---
# SentenceTransformer based on sentence-transformers/clip-ViT-L-14
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/clip-ViT-L-14](https://huggingface.co/sentence-transformers/clip-ViT-L-14) on the [multimodal_lpt2](https://huggingface.co/datasets/machinev/multimodalLPT2) 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](https://huggingface.co/sentence-transformers/clip-ViT-L-14)
- **Maximum Sequence Length:** None tokens
- **Output Dimensionality:** None dimensions
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
- [multimodal_lpt2](https://huggingface.co/datasets/machinev/multimodalLPT2)
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): CLIPModel()
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
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("machinev/model")
# Run inference
sentences = [
'the main power cable is not connected with LPT ',
'/content/sample_data/images/LPT (4).jpeg',
'the main power cable is not connected with LPT ',
]
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-validation`
* Evaluated with [TripletEvaluator
](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator)
| Metric | yt-title-thumbnail-train | yt-title-thumbnail-validation |
|:--------------------|:-------------------------|:------------------------------|
| **cosine_accuracy** | **0.0** | **0.0** |
## Training Details
### Training Dataset
#### multimodal_lpt2
* Dataset: [multimodal_lpt2](https://huggingface.co/datasets/machinev/multimodalLPT2) at [9f649f9](https://huggingface.co/datasets/machinev/multimodalLPT2/tree/9f649f9c95cc375b7ec5895fb47f642f251d288e)
* Size: 12 training samples
* Columns: text
, image_path
, anchor
, positive
, and negative
* Approximate statistics based on the first 12 samples:
| | text | image_path | anchor | positive | negative |
|:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:----------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
| type | string | string | PIL.JpegImagePlugin.JpegImageFile | string | string |
| details |
the main power cable is not connected with LPT
| /content/sample_data/images/LPT (1).jpeg
|
| the main power cable is not connected with LPT
| the main power cable is not connected with LPT
|
| the main power cable is connected with LPT
| /content/sample_data/images/LPT (2).jpeg
|
| the main power cable is connected with LPT
| the main power cable is connected with LPT
|
| the main power cable is connected with LPT
| /content/sample_data/images/LPT (3).jpeg
|
| the main power cable is connected with LPT
| the main power cable is connected with LPT
|
* Loss: [MultipleNegativesRankingLoss
](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim"
}
```
### Evaluation Dataset
#### multimodal_lpt2
* Dataset: [multimodal_lpt2](https://huggingface.co/datasets/machinev/multimodalLPT2) at [9f649f9](https://huggingface.co/datasets/machinev/multimodalLPT2/tree/9f649f9c95cc375b7ec5895fb47f642f251d288e)
* Size: 12 evaluation samples
* Columns: text
, image_path
, anchor
, positive
, and negative
* Approximate statistics based on the first 12 samples:
| | text | image_path | anchor | positive | negative |
|:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:----------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
| type | string | string | PIL.JpegImagePlugin.JpegImageFile | string | string |
| details | the main power cable is not connected with LPT
| /content/sample_data/images/LPT (1).jpeg
|
| the main power cable is not connected with LPT
| the main power cable is not connected with LPT
|
| the main power cable is connected with LPT
| /content/sample_data/images/LPT (2).jpeg
|
| the main power cable is connected with LPT
| the main power cable is connected with LPT
|
| the main power cable is connected with LPT
| /content/sample_data/images/LPT (3).jpeg
|
| the main power cable is connected with LPT
| the main power cable is connected with LPT
|
* Loss: [MultipleNegativesRankingLoss
](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"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