pj-mathematician/JobGTE-multilingual-base-v1

Job - Job matching Alibaba-NLP/gte-multilingual-base (v1)

Top performing model on TalentCLEF 2025 Task A. Use it for multilingual job title matching

Model Details

Model Description

• Model Type: Sentence Transformer
• Base model: Alibaba-NLP/gte-multilingual-base
• Maximum Sequence Length: 512 tokens
• Output Dimensionality: 768 dimensions
• Similarity Function: Cosine Similarity
• Training Datasets:
  • full_en
  • full_de
  • full_es
  • full_zh
  • mix

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: NewModel 
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, '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': False, '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/JobGTE-multilingual-base-v1")
# Run inference
sentences = [
    'Volksvertreter',
    'Parlamentarier',
    'Oberbürgermeister',
]
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]

Evaluation

Metrics

Information Retrieval

• Datasets: full_en, full_es, full_de, full_zh, mix_es, mix_de and mix_zh
• Evaluated with InformationRetrievalEvaluator

Metric	full_en	full_es	full_de	full_zh	mix_es	mix_de	mix_zh
cosine_accuracy@1	0.6571	0.1243	0.2956	0.6602	0.728	0.6703	0.1908
cosine_accuracy@20	0.9905	1.0	0.9704	0.9806	0.96	0.9506	1.0
cosine_accuracy@50	0.9905	1.0	0.9852	0.9903	0.9792	0.9776	1.0
cosine_accuracy@100	0.9905	1.0	0.9852	0.9903	0.9943	0.9865	1.0
cosine_accuracy@150	0.9905	1.0	0.9901	0.9903	0.9958	0.9932	1.0
cosine_accuracy@200	0.9905	1.0	0.9901	0.9903	0.9974	0.9948	1.0
cosine_precision@1	0.6571	0.1243	0.2956	0.6602	0.728	0.6703	0.1908
cosine_precision@20	0.5171	0.5719	0.5084	0.4782	0.1243	0.1252	0.1544
cosine_precision@50	0.316	0.3885	0.3654	0.2895	0.0515	0.0523	0.0618
cosine_precision@100	0.189	0.2517	0.2413	0.1757	0.0263	0.0267	0.0309
cosine_precision@150	0.1338	0.1905	0.1804	0.126	0.0176	0.018	0.0206
cosine_precision@200	0.1043	0.1522	0.1447	0.0982	0.0133	0.0135	0.0154
cosine_recall@1	0.0678	0.0037	0.0111	0.0615	0.2813	0.2524	0.0614
cosine_recall@20	0.547	0.3842	0.3221	0.5108	0.9183	0.9096	1.0
cosine_recall@50	0.74	0.5641	0.5025	0.6923	0.9499	0.9482	1.0
cosine_recall@100	0.8453	0.6742	0.6248	0.8004	0.9701	0.9685	1.0
cosine_recall@150	0.8838	0.7464	0.683	0.8465	0.9768	0.9782	1.0
cosine_recall@200	0.9109	0.7825	0.7216	0.8771	0.9818	0.981	1.0
cosine_ndcg@1	0.6571	0.1243	0.2956	0.6602	0.728	0.6703	0.1908
cosine_ndcg@20	0.6954	0.6139	0.5393	0.654	0.8044	0.7736	0.5474
cosine_ndcg@50	0.715	0.5874	0.5267	0.6707	0.813	0.7844	0.5474
cosine_ndcg@100	0.7679	0.6144	0.5579	0.7234	0.8173	0.7889	0.5474
cosine_ndcg@150	0.7857	0.6499	0.588	0.7438	0.8186	0.7909	0.5474
cosine_ndcg@200	0.797	0.6681	0.6071	0.7554	0.8195	0.7914	0.5474
cosine_mrr@1	0.6571	0.1243	0.2956	0.6602	0.728	0.6703	0.1908
cosine_mrr@20	0.8138	0.5581	0.5104	0.8037	0.7969	0.752	0.4093
cosine_mrr@50	0.8138	0.5581	0.511	0.8041	0.7975	0.7529	0.4093
cosine_mrr@100	0.8138	0.5581	0.511	0.8041	0.7977	0.7531	0.4093
cosine_mrr@150	0.8138	0.5581	0.511	0.8041	0.7977	0.7531	0.4093
cosine_mrr@200	0.8138	0.5581	0.511	0.8041	0.7977	0.7531	0.4093
cosine_map@1	0.6571	0.1243	0.2956	0.6602	0.728	0.6703	0.1908
cosine_map@20	0.5579	0.4799	0.401	0.5087	0.7351	0.6968	0.3298
cosine_map@50	0.5471	0.425	0.3588	0.4926	0.7374	0.6996	0.3298
cosine_map@100	0.5796	0.4302	0.3633	0.5217	0.738	0.7003	0.3298
cosine_map@150	0.5875	0.4459	0.3777	0.5299	0.7381	0.7004	0.3298
cosine_map@200	0.5912	0.4533	0.3848	0.5334	0.7382	0.7005	0.3298
cosine_map@500	0.5953	0.4656	0.3978	0.5386	0.7383	0.7006	0.3298

Training Details

Training Datasets

full_en

full_en

• Dataset: full_en
• Size: 28,880 training samples
• Columns: anchor and positive
• Approximate statistics based on the first 1000 samples:

  	anchor	positive
  type	string	string
  details	min: 3 tokens mean: 5.68 tokens max: 11 tokens	min: 3 tokens mean: 5.76 tokens max: 12 tokens

• Samples:

  anchor	positive
  air commodore	flight lieutenant
  command and control officer	flight officer
  air commodore	command and control officer

• Loss: GISTEmbedLoss 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, 'margin_strategy': 'absolute', 'margin': 0.0}
  

full_de

full_de

• Dataset: full_de
• Size: 23,023 training samples
• Columns: anchor and positive
• Approximate statistics based on the first 1000 samples:

  	anchor	positive
  type	string	string
  details	min: 3 tokens mean: 7.99 tokens max: 30 tokens	min: 3 tokens mean: 8.19 tokens max: 30 tokens

• Samples:

  anchor	positive
  Staffelkommandantin	Kommodore
  Luftwaffenoffizierin	Luftwaffenoffizier/Luftwaffenoffizierin
  Staffelkommandantin	Luftwaffenoffizierin

• Loss: GISTEmbedLoss 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, 'margin_strategy': 'absolute', 'margin': 0.0}
  

full_es

full_es

• Dataset: full_es
• Size: 20,724 training samples
• Columns: anchor and positive
• Approximate statistics based on the first 1000 samples:

  	anchor	positive
  type	string	string
  details	min: 3 tokens mean: 9.13 tokens max: 32 tokens	min: 3 tokens mean: 8.84 tokens max: 32 tokens

• Samples:

  anchor	positive
  jefe de escuadrón	instructor
  comandante de aeronave	instructor de simulador
  instructor	oficial del Ejército del Aire

• Loss: GISTEmbedLoss 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, 'margin_strategy': 'absolute', 'margin': 0.0}
  

full_zh

full_zh

• Dataset: full_zh
• Size: 30,401 training samples
• Columns: anchor and positive
• Approximate statistics based on the first 1000 samples:

  	anchor	positive
  type	string	string
  details	min: 5 tokens mean: 7.15 tokens max: 14 tokens	min: 5 tokens mean: 7.46 tokens max: 21 tokens

• Samples:

  anchor	positive
  技术总监	技术和运营总监
  技术总监	技术主管
  技术总监	技术艺术总监

• Loss: GISTEmbedLoss 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, 'margin_strategy': 'absolute', 'margin': 0.0}
  

mix

mix

• Dataset: mix
• Size: 21,760 training samples
• Columns: anchor and positive
• Approximate statistics based on the first 1000 samples:

  	anchor	positive
  type	string	string
  details	min: 2 tokens mean: 6.71 tokens max: 19 tokens	min: 2 tokens mean: 7.69 tokens max: 19 tokens

• Samples:

  anchor	positive
  technical manager	Technischer Direktor für Bühne, Film und Fernsehen
  head of technical	directora técnica
  head of technical department	技术艺术总监

• Loss: GISTEmbedLoss 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, 'margin_strategy': 'absolute', 'margin': 0.0}
  

Training Hyperparameters

Non-Default Hyperparameters

• eval_strategy: steps
• per_device_train_batch_size: 64
• per_device_eval_batch_size: 128
• gradient_accumulation_steps: 2
• num_train_epochs: 5
• warmup_ratio: 0.05
• log_on_each_node: False
• fp16: True
• dataloader_num_workers: 4
• ddp_find_unused_parameters: True
• batch_sampler: no_duplicates

All Hyperparameters

Click to expand

• overwrite_output_dir: False
• do_predict: False
• eval_strategy: steps
• prediction_loss_only: True
• per_device_train_batch_size: 64
• 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: 5
• 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: []
• fsdp_min_num_params: 0
• fsdp_config: {'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: False
• 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

Training Logs

Epoch	Step	Training Loss	full_en_cosine_ndcg@200	full_es_cosine_ndcg@200	full_de_cosine_ndcg@200	full_zh_cosine_ndcg@200	mix_es_cosine_ndcg@200	mix_de_cosine_ndcg@200	mix_zh_cosine_ndcg@200
-1	-1	-	0.7447	0.6125	0.5378	0.7240	0.7029	0.6345	0.5531
0.0010	1	3.4866	-	-	-	-	-	-	-
0.1027	100	2.5431	-	-	-	-	-	-	-
0.2053	200	1.4536	0.7993	0.6633	0.5974	0.7642	0.7567	0.7011	0.5498
0.3080	300	1.1018	-	-	-	-	-	-	-
0.4107	400	0.9184	0.7925	0.6586	0.6058	0.7587	0.7749	0.7278	0.5486
0.5133	500	0.8973	-	-	-	-	-	-	-
0.6160	600	0.7309	0.7951	0.6671	0.6096	0.7708	0.7793	0.7339	0.5525
0.7187	700	0.7297	-	-	-	-	-	-	-
0.8214	800	0.7281	0.7929	0.6711	0.6088	0.7645	0.7899	0.7444	0.5479
0.9240	900	0.6607	-	-	-	-	-	-	-
1.0267	1000	0.6075	0.7915	0.6659	0.6088	0.7665	0.7968	0.7588	0.5482
1.1294	1100	0.4553	-	-	-	-	-	-	-
1.2320	1200	0.4775	0.7979	0.6696	0.6033	0.7669	0.7959	0.7624	0.5484
1.3347	1300	0.4838	-	-	-	-	-	-	-
1.4374	1400	0.4912	0.7973	0.6757	0.6112	0.7656	0.7978	0.7650	0.5487
1.5400	1500	0.4732	-	-	-	-	-	-	-
1.6427	1600	0.5269	0.8031	0.6723	0.6108	0.7654	0.8008	0.7660	0.5492
1.7454	1700	0.4822	-	-	-	-	-	-	-
1.8480	1800	0.5072	0.7962	0.6668	0.6051	0.7592	0.8001	0.7714	0.5486
1.9507	1900	0.4709	-	-	-	-	-	-	-
2.0544	2000	0.3772	0.7940	0.6647	0.6037	0.7579	0.8064	0.7732	0.5479
2.1571	2100	0.3982	-	-	-	-	-	-	-
2.2598	2200	0.3073	0.7969	0.6652	0.6005	0.7625	0.8054	0.7734	0.5493
2.3624	2300	0.383	-	-	-	-	-	-	-
2.4651	2400	0.3687	0.7925	0.6690	0.5987	0.7583	0.8081	0.7735	0.5477
2.5678	2500	0.3472	-	-	-	-	-	-	-
2.6704	2600	0.3557	0.7956	0.6758	0.6019	0.7659	0.8082	0.7767	0.5491
2.7731	2700	0.3527	-	-	-	-	-	-	-
2.8758	2800	0.3446	0.7945	0.6719	0.6020	0.7616	0.8124	0.7818	0.5496
2.9784	2900	0.3566	-	-	-	-	-	-	-
3.0821	3000	0.3252	0.7948	0.6682	0.6025	0.7617	0.8152	0.7848	0.5516
3.1848	3100	0.2968	-	-	-	-	-	-	-
3.2875	3200	0.2962	0.7953	0.6717	0.6086	0.7613	0.8110	0.7824	0.5482
3.3901	3300	0.3084	-	-	-	-	-	-	-
3.4928	3400	0.2909	0.7940	0.6634	0.6023	0.7615	0.8138	0.7822	0.5457
3.5955	3500	0.2964	-	-	-	-	-	-	-
3.6982	3600	0.3193	0.7960	0.6635	0.6070	0.7534	0.8164	0.7844	0.5467
3.8008	3700	0.3514	-	-	-	-	-	-	-
3.9035	3800	0.3147	0.7973	0.6696	0.6125	0.7616	0.8176	0.7885	0.5469
4.0062	3900	0.2738	-	-	-	-	-	-	-
4.1088	4000	0.2842	0.7960	0.6672	0.6082	0.7536	0.8174	0.7891	0.5479
4.2115	4100	0.2739	-	-	-	-	-	-	-
4.3142	4200	0.2704	0.7979	0.6681	0.6111	0.7540	0.8180	0.7891	0.5476
4.4168	4300	0.2529	-	-	-	-	-	-	-
4.5195	4400	0.272	0.7968	0.6685	0.6087	0.7564	0.8185	0.7901	0.5476
4.6222	4500	0.3	-	-	-	-	-	-	-
4.7248	4600	0.2598	0.7972	0.6675	0.6072	0.7556	0.8190	0.7909	0.5478
4.8275	4700	0.3101	-	-	-	-	-	-	-
4.9302	4800	0.2524	0.7970	0.6681	0.6071	0.7554	0.8195	0.7914	0.5474

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",
}

GISTEmbedLoss

@misc{solatorio2024gistembed,
    title={GISTEmbed: Guided In-sample Selection of Training Negatives for Text Embedding Fine-tuning},
    author={Aivin V. Solatorio},
    year={2024},
    eprint={2402.16829},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}