JobSkillGTE-7b-lora

Job-Skill matching fintuned Alibaba-NLP/gte-Qwen2-7B-instruct lora

Top performing model on TalentCLEF 2025 Task B. Use it for job title <-> skill set matching

Model Details

Model Description

• Model Type: Sentence Transformer
• Base model: Alibaba-NLP/gte-Qwen2-7B-instruct <!-- at revision a8d08b36ada9cacfe34c4d6f80957772a025daf2 -->
• Maximum Sequence Length: 512 tokens
• Output Dimensionality: 3584 dimensions
• Similarity Function: Cosine Similarity

<!-- - **Training Dataset:** Unknown --> <!-- - **Language:** Unknown --> <!-- - **License:** Unknown -->

Model Sources

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

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: Qwen2Model 
  (1): Pooling({'word_embedding_dimension': 3584, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': True, 'include_prompt': True})
  (2): Normalize()
)

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("pj-mathematician/JobSkillGTE-7b-lora")
# Run inference
sentences = [
    "An insulation supervisor, regardless of the specific type of insulation material or installation area, requires strong project management skills, knowledge of building codes and safety regulations, and expertise in insulation techniques to oversee the installation process effectively and ensure quality standards are met.\n['insulation supervisor', 'supervisor of installation of insulating materials', 'supervisor of insulation materials installation', 'supervisor of installation of insulation', 'solid wall insulation installation supervisor', 'insulation installers supervisor', 'cavity wall insulation installation supervisor', 'loft insulation installation supervisor']",
    "The skill of installing insulation material is primarily required by job roles such as insulation workers, HVAC technicians, and construction specialists, who are responsible for improving energy efficiency and thermal comfort in buildings by correctly fitting and fixing insulation materials in various structures.\n['install insulation material', 'insulate structure', 'fix insulation', 'insulation material installation', 'installation of insulation material', 'fitting insulation', 'insulating structure', 'installing insulation material', 'fixing insulation', 'fit insulation']",
    "Job roles such as Food Safety Inspector, Public Health Officer, and Environmental Health Specialist require the skill of taking action on food safety violations to ensure compliance with health regulations and maintain public safety standards.\n['take action on food safety violations', 'invoke action on food safety violations', 'agree action on food safety violations', 'pursue action on food safety violations', 'determine action on food safety violations']",
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 3584]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]

<!-- ### Direct Usage (Transformers) <details><summary>Click to see the direct usage in Transformers</summary> </details> --> <!-- ### Downstream Usage (Sentence Transformers) You can finetune this model on your own dataset. <details><summary>Click to expand</summary> </details> --> <!-- ### Out-of-Scope Use *List how the model may foreseeably be misused and address what users ought not to do with the model.* --> <!-- ## Bias, Risks and Limitations *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* --> <!-- ### Recommendations *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* -->

Training Details

Training Dataset

Unnamed Dataset

• Size: 114,699 training samples
• Columns: <code>anchor</code> and <code>positive</code>
• Approximate statistics based on the first 1000 samples:

  	anchor	positive
  type	string	string
  details	<ul><li>min: 73 tokens</li><li>mean: 133.53 tokens</li><li>max: 333 tokens</li></ul>	<ul><li>min: 44 tokens</li><li>mean: 104.56 tokens</li><li>max: 236 tokens</li></ul>

• Samples:

  anchor	positive
  <code>A technical director or any of its synonyms requires a strong blend of technical expertise and leadership skills, including the ability to oversee technical operations, manage teams, and ensure the successful execution of technical projects while maintaining operational efficiency and innovation.<br>['technical director', 'technical and operations director', 'head of technical', 'director of technical arts', 'head of technical department', 'technical supervisor', 'technical manager']</code>	<code>Job roles that require promoting health and safety include occupational health and safety specialists, safety managers, and public health educators, all of whom work to ensure safe and healthy environments in workplaces and communities.<br>['promote health and safety', 'promote importance of health and safety', 'promoting health and safety', 'advertise health and safety']</code>
  <code>A technical director or any of its synonyms requires a strong blend of technical expertise and leadership skills, including the ability to oversee technical operations, manage teams, and ensure the successful execution of technical projects while maintaining operational efficiency and innovation.<br>['technical director', 'technical and operations director', 'head of technical', 'director of technical arts', 'head of technical department', 'technical supervisor', 'technical manager']</code>	<code>Job roles that require organizing rehearsals include directors, choreographers, and conductors in theater, dance, and music ensembles, who must efficiently plan and schedule practice sessions to prepare performers for a successful final performance.<br>['organise rehearsals', 'organise rehearsal', 'organize rehearsals', 'plan rehearsals', 'arrange rehearsals', 'organising rehearsals', 'schedule rehearsals']</code>
  <code>A technical director or any of its synonyms requires a strong blend of technical expertise and leadership skills, including the ability to oversee technical operations, manage teams, and ensure the successful execution of technical projects while maintaining operational efficiency and innovation.<br>['technical director', 'technical and operations director', 'head of technical', 'director of technical arts', 'head of technical department', 'technical supervisor', 'technical manager']</code>	<code>Job roles such as Health and Safety Managers, Environmental Health Officers, and Risk Management Specialists often require the skill of negotiating health and safety issues with third parties to ensure compliance and protection standards are met across different organizations and sites.<br>['negotiate health and safety issues with third parties', 'agree with third parties on health and safety', 'negotiate issues on health and safety with third parties', 'negotiate with third parties on health and safety issues', 'negotiate health and safety matters with third parties']</code>

• Loss: <code>CachedGISTEmbedLoss</code> with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel 
    (1): Pooling({'word_embedding_dimension': 384, '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})
    (2): Normalize()
  ), 'temperature': 0.01, 'mini_batch_size': 48, 'margin_strategy': 'absolute', 'margin': 0.0}
  

Training Hyperparameters

Non-Default Hyperparameters

• per_device_train_batch_size: 128
• per_device_eval_batch_size: 128
• gradient_accumulation_steps: 2
• num_train_epochs: 2
• warmup_ratio: 0.05
• log_on_each_node: False
• fp16: True
• dataloader_num_workers: 4
• fsdp: ['full_shard', 'auto_wrap']
• fsdp_config: {'transformer_layer_cls_to_wrap': ['Qwen2DecoderLayer'], 'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
• ddp_find_unused_parameters: True
• gradient_checkpointing: True
• batch_sampler: no_duplicates

All Hyperparameters

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

• overwrite_output_dir: False
• do_predict: False
• eval_strategy: no
• prediction_loss_only: True
• per_device_train_batch_size: 128
• per_device_eval_batch_size: 128
• per_gpu_train_batch_size: None
• per_gpu_eval_batch_size: None
• gradient_accumulation_steps: 2
• eval_accumulation_steps: None
• torch_empty_cache_steps: None
• learning_rate: 5e-05
• 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: 0.05
• warmup_steps: 0
• log_level: passive
• log_level_replica: warning
• log_on_each_node: False
• 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: True
• 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: True
• dataloader_num_workers: 4
• 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: ['full_shard', 'auto_wrap']
• fsdp_min_num_params: 0
• fsdp_config: {'transformer_layer_cls_to_wrap': ['Qwen2DecoderLayer'], 'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
• tp_size: 0
• 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: True
• 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
• gradient_checkpointing: True
• 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
• eval_use_gather_object: False
• average_tokens_across_devices: False
• prompts: None
• batch_sampler: no_duplicates
• multi_dataset_batch_sampler: proportional

</details>

Training Logs

Framework Versions

• Python: 3.11.11
• Sentence Transformers: 4.1.0
• Transformers: 4.51.2
• PyTorch: 2.6.0+cu124
• Accelerate: 1.6.0
• Datasets: 3.5.0
• Tokenizers: 0.21.1

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",
}

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