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Ghani-25
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LF-enrich-sim-matryoshka-64

Sentence Similarity
sentence-transformers
Safetensors
multilingual
xlm-roberta
feature-extraction
Generated from Trainer
loss:MatryoshkaLoss
Eval Results
text-embeddings-inference
Inference Endpoints
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LF-enrich-sim-matryoshka-64 / README.md
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metadata
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 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
  • 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:

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("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]

Evaluation

Metrics

Semantic Similarity

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

Training Details

Training Dataset

json

  • Dataset: json
  • Columns: sentence1, sentence2, and label
  • Approximate statistics based on the first 1000 samples:
    sentence1 sentence2 label
    type string string float
    details
    • min: 3 tokens
    • mean: 10.22 tokens
    • max: 30 tokens
    • min: 3 tokens
    • mean: 9.98 tokens
    • max: 67 tokens
    • min: -0.05
    • mean: 0.37
    • max: 0.98
  • Samples:
    sentence1 sentence2 label
    Contributive filmer Doctorant contractuel (2016-2019) 0.20986526
    Responsable Développement et Communication Bilingual Business Assistant 0.3238712
    Law Trainee Sales Director contract manager 0.24983984
  • Loss: MatryoshkaLoss with these parameters:
    {
    "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