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---
language:
- multilingual
license: apache-2.0
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- loss:MatryoshkaLoss
base_model: Ghani-25/LF_enrich_sim
widget:
- source_sentence: CTO and co-Founder
sentences:
- Responsable surpervision des départements
- Senior sales executive
- >-
Injection Operations Supervisor - Industrial Efficiency - Systems &
Equipment
- source_sentence: Commercial Account Executive
sentences:
- Automation Electrician
- Love Coach Extra
- Psychologue Clinicienne (Croix Rouge Française) Hébergements et ESAT
- source_sentence: Chargée d'etudes actuarielles IFRS17
sentences:
- Visuel Merchandiser Shop In Shop
- VIP Lounge Hostess
- Directeur Adjoint des opérations
- source_sentence: Cheffe de projet emailing
sentences:
- Experte Territoriale
- Responsable Clientele / Commerciale et Communication /
- STRATEGIC CONSULTANT - LIVE BUSINESS CASE
- source_sentence: 'Summer Job: Export Manager'
sentences:
- Clinical Project Leader
- Member and Maghreb Representative
- Responsable Export Afrique Amériques
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- pearson_cosine
- spearman_cosine
model-index:
- name: Our original base similarity Matryoshka
results:
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: dim 768
type: dim_768
metrics:
- type: pearson_cosine
value: 0.9696182810336916
name: Pearson Cosine
- type: spearman_cosine
value: 0.9472439476744547
name: Spearman Cosine
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: dim 512
type: dim_512
metrics:
- type: pearson_cosine
value: 0.9692898932305203
name: Pearson Cosine
- type: spearman_cosine
value: 0.9466297549051846
name: Spearman Cosine
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: dim 256
type: dim_256
metrics:
- type: pearson_cosine
value: 0.9662306280132803
name: Pearson Cosine
- type: spearman_cosine
value: 0.9407689506959847
name: Spearman Cosine
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: dim 128
type: dim_128
metrics:
- type: pearson_cosine
value: 0.960638838395904
name: Pearson Cosine
- type: spearman_cosine
value: 0.9314825034513964
name: Spearman Cosine
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: dim 64
type: dim_64
metrics:
- type: pearson_cosine
value: 0.9463950305830967
name: Pearson Cosine
- type: spearman_cosine
value: 0.9100801085031441
name: Spearman Cosine
---
# Our original base similarity Matryoshka
This is a [sentence-transformers] model finetuned from [Ghani-25/LF_enrich_sim](https://huggingface.co/Ghani-25/LF_enrich_sim) on the json dataset. It maps sentences & paragraphs to a 768-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:** [Ghani-25/LF_enrich_sim](https://huggingface.co/Ghani-25/LF_enrich_sim) <!-- at revision fb09bbe3ab4baafa2101c33989bf2ed8ffddf5cc -->
- **Maximum Sequence Length:** 128 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
- json
- **Language:** multilingual
- **License:** apache-2.0
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: XLMRobertaModel
(1): Pooling({'word_embedding_dimension': 768, '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})
)
```
## 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("Ghani-25/LF-enrich-sim-matryoshka-64")
# Run inference
sentences = [
'Summer Job: Export Manager',
'Responsable Export Afrique Amériquess
'Clinical Project Leader',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
# Extraction de la diagonale pour obtenir les similarités correspondantes
similarities_diagonal = similarities.diag().cpu().numpy()
print(similarities_diagonal)
# [0.896542]
```
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## Evaluation
### Metrics
#### Semantic Similarity
* Datasets: `dim_768`, `dim_512`, `dim_256`, `dim_128` and `dim_64`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | dim_768 | dim_512 | dim_256 | dim_128 | dim_64 |
|:--------------------|:-----------|:-----------|:-----------|:-----------|:-----------|
| pearson_cosine | 0.9696 | 0.9693 | 0.9662 | 0.9606 | 0.9464 |
| **spearman_cosine** | **0.9472** | **0.9466** | **0.9408** | **0.9315** | **0.9101** |
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## Training Details
### Training Dataset
#### json
* Dataset: json
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | label |
|:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:------------------------------------------------------------------|
| type | string | string | float |
| details | <ul><li>min: 3 tokens</li><li>mean: 10.22 tokens</li><li>max: 30 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 9.98 tokens</li><li>max: 67 tokens</li></ul> | <ul><li>min: -0.05</li><li>mean: 0.37</li><li>max: 0.98</li></ul> |
* Samples:
| sentence1 | sentence2 | label |
|:--------------------------------------------------------|:-----------------------------------------------|:------------------------|
| <code>Contributive filmer</code> | <code>Doctorant contractuel (2016-2019)</code> | <code>0.20986526</code> |
| <code>Responsable Développement et Communication</code> | <code>Bilingual Business Assistant</code> | <code>0.3238712</code> |
| <code>Law Trainee</code> | <code>Sales Director contract manager</code> | <code>0.24983984</code> |
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
```json
{
"loss": "CosineSimilarityLoss",
"matryoshka_dims": [
768,
512,
256,
128,
64
],
"matryoshka_weights": [
1,
1,
1,
1,
1
],
"n_dims_per_step": -1
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: epoch
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 16
- `gradient_accumulation_steps`: 16
- `learning_rate`: 2e-05
- `num_train_epochs`: 4
- `lr_scheduler_type`: cosine
- `warmup_ratio`: 0.1
- `bf16`: True
- `tf32`: True
- `load_best_model_at_end`: True
- `optim`: adamw_torch_fused
#### All Hyperparameters
Contact the author.
### Training Logs
| Epoch | Step | Training Loss | dim_768_spearman_cosine | dim_512_spearman_cosine | dim_256_spearman_cosine | dim_128_spearman_cosine | dim_64_spearman_cosine |
|:----------:|:-------:|:-------------:|:-----------------------:|:-----------------------:|:-----------------------:|:-----------------------:|:----------------------:|
| 0.1624 | 10 | 0.0669 | - | - | - | - | - |
| 0.3249 | 20 | 0.0563 | - | - | - | - | - |
| 0.4873 | 30 | 0.0496 | - | - | - | - | - |
| 0.6497 | 40 | 0.0456 | - | - | - | - | - |
| 0.8122 | 50 | 0.0418 | - | - | - | - | - |
| 0.9746 | 60 | 0.0407 | - | - | - | - | - |
| 0.9909 | 61 | - | 0.9223 | 0.9199 | 0.9087 | 0.8920 | 0.8586 |
| 1.1371 | 70 | 0.0326 | - | - | - | - | - |
| 1.2995 | 80 | 0.0312 | - | - | - | - | - |
| 1.4619 | 90 | 0.0303 | - | - | - | - | - |
| 1.6244 | 100 | 0.03 | - | - | - | - | - |
| 1.7868 | 110 | 0.0291 | - | - | - | - | - |
| 1.9492 | 120 | 0.0301 | - | - | - | - | - |
| 1.9980 | 123 | - | 0.9393 | 0.9382 | 0.9304 | 0.9191 | 0.8946 |
| 2.1117 | 130 | 0.0257 | - | - | - | - | - |
| 2.2741 | 140 | 0.0243 | - | - | - | - | - |
| 2.4365 | 150 | 0.0246 | - | - | - | - | - |
| 2.5990 | 160 | 0.0235 | - | - | - | - | - |
| 2.7614 | 170 | 0.024 | - | - | - | - | - |
| 2.9239 | 180 | 0.023 | - | - | - | - | - |
| 2.9888 | 184 | - | 0.9464 | 0.9457 | 0.9396 | 0.9301 | 0.9083 |
| 3.0863 | 190 | 0.0222 | - | - | - | - | - |
| 3.2487 | 200 | 0.022 | - | - | - | - | - |
| 3.4112 | 210 | 0.022 | - | - | - | - | - |
| 3.5736 | 220 | 0.0226 | - | - | - | - | - |
| 3.7360 | 230 | 0.021 | - | - | - | - | - |
| 3.8985 | 240 | 0.0224 | - | - | - | - | - |
| **3.9635** | **244** | **-** | **0.9472** | **0.9466** | **0.9408** | **0.9315** | **0.9101** |
* The bold row denotes the saved checkpoint.
### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.3.1
- Transformers: 4.41.2
- PyTorch: 2.5.1+cu121
- Accelerate: 1.1.1
- Datasets: 2.19.1
- Tokenizers: 0.19.1
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