Modelpublic

Yaaasmineee/sbert-cv-job-matching

sourceHugging Faceupdated 3mo agoView on Hugging Face

1likes89downloads

Model Card

SentenceTransformer based on sentence-transformers/paraphrase-multilingual-mpnet-base-v2

This is a sentence-transformers model finetuned from sentence-transformers/paraphrase-multilingual-mpnet-base-v2. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for retrieval.

Model Details

Model Description

—Model Type: Sentence Transformer
—Base model: sentence-transformers/paraphrase-multilingual-mpnet-base-v2
—Maximum Sequence Length: 128 tokens
—Output Dimensionality: 768 dimensions
—Similarity Function: Cosine Similarity
—Supported Modality: Text

Model Sources

—Documentation: Sentence Transformers Documentation
—Repository: Sentence Transformers on GitHub
—Hugging Face: Sentence Transformers on Hugging Face

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'transformer_task': 'feature-extraction', 'modality_config': {'text': {'method': 'forward', 'method_output_name': 'last_hidden_state'}}, 'module_output_name': 'token_embeddings', 'architecture': 'XLMRobertaModel'})
  (1): Pooling({'embedding_dimension': 768, 'pooling_mode': 'mean', '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("Yaaasmineee/sbert-cv-job-matching")
# Run inference
sentences = [
    'Develop caching mechanisms using Redis for backend systems.',
    'Design identity and access management for APIs.',
    'Write and update SOPs and documentation.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7568, 0.2896],
#         [0.7568, 1.0000, 0.3603],
#         [0.2896, 0.3603, 1.0000]])

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Downstream Usage (Sentence Transformers)

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Training Details

Training Dataset

Unnamed Dataset

—Size: 6,875 training samples
—Columns: <code>sentence0</code>, <code>sentence1</code>, and <code>label</code>
—Approximate statistics based on the first 100 samples: | | sentence0 | sentence1 | label | |:---------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------| | type | string | string | float | | modality | text | text | | | details | <ul><li>min: 8 tokens</li><li>mean: 20.62 tokens</li><li>max: 58 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 20.26 tokens</li><li>max: 57 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.42</li><li>max: 1.0</li></ul> |
—Samples: | sentence0 | sentence1 | label | |:---------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------|:------------------| | <code>Risepoint is seeking an Enterprise Architect to define and drive enterprise technology architecture across platforms, integrations, data, and security.</code> | <code>Integrate GitOps workflows (e.g., Argo CD) where beneficial.</code> | <code>0.25</code> | | <code>Transform monolithic backend into microservices.</code> | <code>Transform monolithic backend into microservices.</code> | <code>1.0</code> | | <code>Create highly available system architecture with redundancy.</code> | <code>Assess reliability in scalable system architecture.</code> | <code>0.75</code> |
—Loss: <code>CosineSimilarityLoss</code> with these parameters:

json

  {
      "loss_fct": "torch.nn.modules.loss.MSELoss",
      "cos_score_transformation": "torch.nn.modules.linear.Identity"
  }

Training Hyperparameters

Non-Default Hyperparameters

—num_train_epochs: 4
—multi_dataset_batch_sampler: round_robin

All Hyperparameters

<details><summary>Click to expand</summary>

—per_device_train_batch_size: 8
—num_train_epochs: 4
—max_steps: -1
—learning_rate: 5e-05
—lr_scheduler_type: linear
—lr_scheduler_kwargs: None
—warmup_steps: 0
—optim: adamwtorchfused
—optim_args: None
—weight_decay: 0.0
—adam_beta1: 0.9
—adam_beta2: 0.999
—adam_epsilon: 1e-08
—optim_target_modules: None
—gradient_accumulation_steps: 1
—average_tokens_across_devices: True
—max_grad_norm: 1
—label_smoothing_factor: 0.0
—bf16: False
—fp16: False
—bf16_full_eval: False
—fp16_full_eval: False
—tf32: None
—gradient_checkpointing: False
—gradient_checkpointing_kwargs: None
—torch_compile: False
—torch_compile_backend: None
—torch_compile_mode: None
—use_liger_kernel: False
—liger_kernel_config: None
—use_cache: False
—neftune_noise_alpha: None
—torch_empty_cache_steps: None
—auto_find_batch_size: False
—log_on_each_node: True
—logging_nan_inf_filter: True
—include_num_input_tokens_seen: no
—log_level: passive
—log_level_replica: warning
—disable_tqdm: False
—project: huggingface
—trackio_space_id: None
—trackio_bucket_id: None
—trackio_static_space_id: None
—per_device_eval_batch_size: 8
—prediction_loss_only: True
—eval_on_start: False
—eval_do_concat_batches: True
—eval_use_gather_object: False
—eval_accumulation_steps: None
—include_for_metrics: []
—batch_eval_metrics: False
—save_only_model: False
—save_on_each_node: False
—enable_jit_checkpoint: False
—push_to_hub: False
—hub_private_repo: None
—hub_model_id: None
—hub_strategy: every_save
—hub_always_push: False
—hub_revision: None
—load_best_model_at_end: False
—ignore_data_skip: False
—restore_callback_states_from_checkpoint: False
—full_determinism: False
—seed: 42
—data_seed: None
—use_cpu: False
—accelerator_config: {'splitbatches': False, 'dispatchbatches': None, 'evenbatches': True, 'useseedablesampler': True, 'nonblocking': False, 'gradientaccumulationkwargs': None}
—parallelism_config: None
—dataloader_drop_last: False
—dataloader_num_workers: 0
—dataloader_pin_memory: True
—dataloader_persistent_workers: False
—dataloader_prefetch_factor: None
—remove_unused_columns: True
—label_names: None
—train_sampling_strategy: random
—length_column_name: length
—ddp_find_unused_parameters: None
—ddp_bucket_cap_mb: None
—ddp_broadcast_buffers: False
—ddp_static_graph: None
—ddp_backend: None
—ddp_timeout: 1800
—fsdp: None
—fsdp_config: None
—deepspeed: None
—debug: []
—skip_memory_metrics: True
—do_predict: False
—resume_from_checkpoint: None
—warmup_ratio: None
—local_rank: -1
—prompts: None
—batch_sampler: batch_sampler
—multi_dataset_batch_sampler: round_robin
—router_mapping: {}
—learning_rate_mapping: {}

</details>

Training Logs

Epoch	Step	Training Loss
0.5814	500	0.0294
1.1628	1000	0.0172
1.7442	1500	0.0088
2.3256	2000	0.0064
2.9070	2500	0.0041
3.4884	3000	0.0026

Training Time

—Training: 13.5 minutes

Framework Versions

—Python: 3.12.13
—Sentence Transformers: 5.5.1
—Transformers: 5.10.2
—PyTorch: 2.11.0+cu128
—Accelerate: 1.13.0
—Datasets: 4.0.0
—Tokenizers: 0.22.2

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",
}

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