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tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:124788
  - loss:CachedGISTEmbedLoss
base_model: Alibaba-NLP/gte-Qwen2-7B-instruct
widget:
  - source_sentence: 其他机械、设备和有形货物租赁服务代表
    sentences:
      - 其他机械和设备租赁服务工作人员
      - 电子和电信设备及零部件物流经理
      - 工业主厨
  - source_sentence: 公交车司机
    sentences:
      - 表演灯光设计师
      - 乙烯基地板安装工
      - 国际巴士司机
  - source_sentence: online communication manager
    sentences:
      - trades union official
      - social media manager
      - budget manager
  - source_sentence: Projektmanagerin
    sentences:
      - Projektmanager/Projektmanagerin
      - Category-Manager
      - Infanterist
  - source_sentence: Volksvertreter
    sentences:
      - Parlamentarier
      - Oberbürgermeister
      - Konsul
pipeline_tag: sentence-similarity
library_name: sentence-transformers

Job - Job matching finetuned Alibaba-NLP/gte-Qwen2-7B-instruct

Best 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-Qwen2-7B-instruct
Maximum Sequence Length: 512 tokens
Output Dimensionality: 3584 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: 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/JobGTE-7b-Lora")
# Run inference
sentences = [
    'Volksvertreter',
    'Parlamentarier',
    'Oberbürgermeister',
]
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]

Training Details

Training Datasets

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: 2 tokens
mean: 4.4 tokens
max: 9 tokens

min: 2 tokens
mean: 4.42 tokens
max: 10 tokens
Samples:

anchor positive

air commodore flight lieutenant

command and control officer flight officer

air commodore command and control officer

	anchor	positive
type	string	string
details	min: 2 tokens mean: 4.4 tokens max: 9 tokens	min: 2 tokens mean: 4.42 tokens max: 10 tokens

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

Loss: CachedGISTEmbedLoss 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': 64, 'margin_strategy': 'absolute', 'margin': 0.0}

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: 2 tokens
mean: 9.11 tokens
max: 33 tokens

min: 2 tokens
mean: 9.41 tokens
max: 33 tokens
Samples:

anchor positive

Staffelkommandantin Kommodore

Luftwaffenoffizierin Luftwaffenoffizier/Luftwaffenoffizierin

Staffelkommandantin Luftwaffenoffizierin

	anchor	positive
type	string	string
details	min: 2 tokens mean: 9.11 tokens max: 33 tokens	min: 2 tokens mean: 9.41 tokens max: 33 tokens

anchor	positive
`Staffelkommandantin`	`Kommodore`
`Luftwaffenoffizierin`	`Luftwaffenoffizier/Luftwaffenoffizierin`
`Staffelkommandantin`	`Luftwaffenoffizierin`

Loss: CachedGISTEmbedLoss 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': 64, 'margin_strategy': 'absolute', 'margin': 0.0}

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: 9.42 tokens
max: 35 tokens

min: 3 tokens
mean: 9.18 tokens
max: 35 tokens
Samples:

anchor positive

jefe de escuadrón instructor

comandante de aeronave instructor de simulador

instructor oficial del Ejército del Aire

	anchor	positive
type	string	string
details	min: 3 tokens mean: 9.42 tokens max: 35 tokens	min: 3 tokens mean: 9.18 tokens max: 35 tokens

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

Loss: CachedGISTEmbedLoss 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': 64, 'margin_strategy': 'absolute', 'margin': 0.0}

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: 3 tokens
mean: 4.7 tokens
max: 12 tokens

min: 3 tokens
mean: 5.04 tokens
max: 19 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

	anchor	positive
type	string	string
details	min: 3 tokens mean: 4.7 tokens max: 12 tokens	min: 3 tokens mean: 5.04 tokens max: 19 tokens

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: CachedGISTEmbedLoss 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': 64, 'margin_strategy': 'absolute', 'margin': 0.0}

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: 1 tokens
mean: 4.98 tokens
max: 14 tokens

min: 1 tokens
mean: 7.22 tokens
max: 27 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

	anchor	positive
type	string	string
details	min: 1 tokens mean: 4.98 tokens max: 14 tokens	min: 1 tokens mean: 7.22 tokens max: 27 tokens

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: CachedGISTEmbedLoss 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': 64, '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

Click to expand

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

Training Logs

Epoch	Step	Training Loss
0.0165	1	4.5178
0.0331	2	3.8803
0.0496	3	2.8882
0.0661	4	4.5362
0.0826	5	3.6406
0.0992	6	3.5285
0.1157	7	4.1398
0.1322	8	4.1543
0.1488	9	4.4487
0.1653	10	4.7408
0.1818	11	2.1874
0.1983	12	3.3176
0.2149	13	2.8286
0.2314	14	2.87
0.2479	15	2.4834
0.2645	16	2.7856
0.2810	17	3.1948
0.2975	18	2.1755
0.3140	19	1.9861
0.3306	20	2.0536
0.3471	21	2.7626
0.3636	22	1.6489
0.3802	23	2.078
0.3967	24	1.5864
0.4132	25	1.8815
0.4298	26	1.8041
0.4463	27	1.7482
0.4628	28	1.191
0.4793	29	1.4166
0.4959	30	1.3215
0.5124	31	1.2907
0.5289	32	1.1294
0.5455	33	1.1586
0.5620	34	1.551
0.5785	35	1.3628
0.5950	36	0.9899
0.6116	37	1.1846
0.6281	38	1.2721
0.6446	39	1.1261
0.6612	40	0.9535
0.6777	41	1.2086
0.6942	42	0.7472
0.7107	43	1.0324
0.7273	44	1.0397
0.7438	45	1.185
0.7603	46	1.2112
0.7769	47	0.84
0.7934	48	0.9286
0.8099	49	0.8689
0.8264	50	0.9546
0.8430	51	0.8283
0.8595	52	0.757
0.8760	53	0.9199
0.8926	54	0.7404
0.9091	55	1.0995
0.9256	56	0.8231
0.9421	57	0.6297
0.9587	58	0.9869
0.9752	59	0.9597
0.9917	60	0.7025
1.0	61	0.4866

Framework Versions

Python: 3.11.11
Sentence Transformers: 4.1.0
Transformers: 4.51.3
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",
}