README.md

metadata

tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:713657
  - loss:MultipleNegativesRankingLoss
base_model: TechWolf/ConTeXT-Skill-Extraction-base
widget:
  - source_sentence: >-
      Successfully gather data from diverse sources, ensuring data integrity and
      accuracy.
    sentences:
      - gather data
      - set up basic recording
      - use global distribution system
  - source_sentence: Learn to mix paints efficiently and effectively in this hands-on workshop.
    sentences:
      - design optical prototypes
      - manage production of footwear or leather goods
      - prepare colour mixtures
  - source_sentence: >-
      Developed and implemented quality control measures for pre-stitching
      processes and techniques for footwear and leather goods.
    sentences:
      - operate heavy construction machinery without supervision
      - pre-stitching processes and techniques for footwear and leather goods
      - deburring machine parts
  - source_sentence: >-
      Improved efficiency in preparing fruits and vegetables for pre-processing
      by implementing optimized workflows.
    sentences:
      - think proactively
      - recommend orthopedic goods to customers depending on their condition
      - prepare fruits and vegetables for pre-processing
  - source_sentence: >-
      Skilled in mixing construction grouts while adhering to strict
      contamination control measures.
    sentences:
      - oversee logistics of finished products
      - think creatively
      - mix construction grouts
pipeline_tag: sentence-similarity
library_name: sentence-transformers

SentenceTransformer based on TechWolf/ConTeXT-Skill-Extraction-base

This is a sentence-transformers model finetuned from TechWolf/ConTeXT-Skill-Extraction-base. 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: TechWolf/ConTeXT-Skill-Extraction-base
• Maximum Sequence Length: 512 tokens
• Output Dimensionality: 768 dimensions
• Similarity Function: Cosine Similarity

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: MPNetModel 
  (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("sentence_transformers_model_id")
# Run inference
sentences = [
    'Skilled in mixing construction grouts while adhering to strict contamination control measures.',
    'mix construction grouts',
    'oversee logistics of finished products',
]
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]

Training Details

Training Dataset

Unnamed Dataset

• Size: 713,657 training samples
• Columns: sentence_0, sentence_1, and label
• Approximate statistics based on the first 1000 samples:

  	sentence_0	sentence_1	label
  type	string	string	float
  details	min: 7 tokens mean: 15.73 tokens max: 61 tokens	min: 3 tokens mean: 6.17 tokens max: 16 tokens	min: 1.0 mean: 1.0 max: 1.0

• Samples:

  sentence_0	sentence_1	label
  Successfully provided stabilisation care in emergency situations, contributing to positive patient outcomes.	provide stabilisation care in emergency	1.0
  This training program covers advanced methods to remove coating from delicate components.	remove coating	1.0
  Utilized statistical modelling to analyse booking patterns and forecast future demand.	analyse booking patterns	1.0

• Loss: MultipleNegativesRankingLoss with these parameters:

  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  

Training Hyperparameters

Non-Default Hyperparameters

• per_device_train_batch_size: 16
• per_device_eval_batch_size: 16
• fp16: True
• multi_dataset_batch_sampler: round_robin

All Hyperparameters

Click to expand

• overwrite_output_dir: False
• do_predict: False
• eval_strategy: no
• prediction_loss_only: True
• per_device_train_batch_size: 16
• per_device_eval_batch_size: 16
• per_gpu_train_batch_size: None
• per_gpu_eval_batch_size: None
• gradient_accumulation_steps: 1
• 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
• num_train_epochs: 3
• max_steps: -1
• lr_scheduler_type: linear
• lr_scheduler_kwargs: {}
• warmup_ratio: 0.0
• warmup_steps: 0
• log_level: passive
• log_level_replica: warning
• log_on_each_node: True
• 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: False
• dataloader_num_workers: 0
• 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}
• 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: None
• 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
• dispatch_batches: None
• split_batches: 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: batch_sampler
• multi_dataset_batch_sampler: round_robin

Training Logs

Click to expand

Epoch	Step	Training Loss
0.0112	500	0.276
0.0224	1000	0.2541
0.0336	1500	0.2507
0.0448	2000	0.2276
0.0560	2500	0.2356
0.0673	3000	0.2153
0.0785	3500	0.2166
0.0897	4000	0.2108
0.1009	4500	0.2011
0.1121	5000	0.2098
0.1233	5500	0.2018
0.1345	6000	0.1879
0.1457	6500	0.1954
0.1569	7000	0.1927
0.1681	7500	0.1983
0.1794	8000	0.1833
0.1906	8500	0.1893
0.2018	9000	0.1992
0.2130	9500	0.1838
0.2242	10000	0.1713
0.2354	10500	0.1839
0.2466	11000	0.1727
0.2578	11500	0.1777
0.2690	12000	0.1691
0.2802	12500	0.1777
0.2915	13000	0.1627
0.3027	13500	0.1647
0.3139	14000	0.1627
0.3251	14500	0.1546
0.3363	15000	0.1689
0.3475	15500	0.1462
0.3587	16000	0.1492
0.3699	16500	0.158
0.3811	17000	0.1537
0.3923	17500	0.1597
0.4036	18000	0.1567
0.4148	18500	0.1607
0.4260	19000	0.1629
0.4372	19500	0.1418
0.4484	20000	0.1606
0.4596	20500	0.1537
0.4708	21000	0.1463
0.4820	21500	0.1372
0.4932	22000	0.1466
0.5044	22500	0.1349
0.5156	23000	0.1586
0.5269	23500	0.1365
0.5381	24000	0.1321
0.5493	24500	0.1549
0.5605	25000	0.1399
0.5717	25500	0.1283
0.5829	26000	0.1423
0.5941	26500	0.1355
0.6053	27000	0.1443
0.6165	27500	0.1417
0.6277	28000	0.1452
0.6390	28500	0.1395
0.6502	29000	0.1422
0.6614	29500	0.1262
0.6726	30000	0.1289
0.6838	30500	0.1363
0.6950	31000	0.1372
0.7062	31500	0.1272
0.7174	32000	0.1309
0.7286	32500	0.1291
0.7398	33000	0.1297
0.7511	33500	0.1226
0.7623	34000	0.1311
0.7735	34500	0.1201
0.7847	35000	0.1363
0.7959	35500	0.1306
0.8071	36000	0.1223
0.8183	36500	0.1173
0.8295	37000	0.1242
0.8407	37500	0.125
0.8519	38000	0.1384
0.8632	38500	0.1196
0.8744	39000	0.1251
0.8856	39500	0.1201
0.8968	40000	0.1199
0.9080	40500	0.1298
0.9192	41000	0.1223
0.9304	41500	0.1335
0.9416	42000	0.1194
0.9528	42500	0.1124
0.9640	43000	0.1127
0.9752	43500	0.1126
0.9865	44000	0.1242
0.9977	44500	0.1241
1.0089	45000	0.1061
1.0201	45500	0.084
1.0313	46000	0.1004
1.0425	46500	0.0898
1.0537	47000	0.0921
1.0649	47500	0.097
1.0761	48000	0.0877
1.0873	48500	0.098
1.0986	49000	0.1078
1.1098	49500	0.0947
1.1210	50000	0.1051
1.1322	50500	0.0981
1.1434	51000	0.0965
1.1546	51500	0.0893
1.1658	52000	0.0969
1.1770	52500	0.097
1.1882	53000	0.1023
1.1994	53500	0.1036
1.2107	54000	0.0903
1.2219	54500	0.1
1.2331	55000	0.0949
1.2443	55500	0.0893
1.2555	56000	0.0966
1.2667	56500	0.094
1.2779	57000	0.0955
1.2891	57500	0.0917
1.3003	58000	0.084
1.3115	58500	0.0859
1.3228	59000	0.0888
1.3340	59500	0.0847
1.3452	60000	0.0846
1.3564	60500	0.0868
1.3676	61000	0.0904
1.3788	61500	0.0848
1.3900	62000	0.0929
1.4012	62500	0.0851
1.4124	63000	0.0989
1.4236	63500	0.0814
1.4348	64000	0.0881
1.4461	64500	0.0909
1.4573	65000	0.0951
1.4685	65500	0.0856
1.4797	66000	0.0914
1.4909	66500	0.0932
1.5021	67000	0.0855
1.5133	67500	0.09
1.5245	68000	0.0801
1.5357	68500	0.087
1.5469	69000	0.0866
1.5582	69500	0.0867
1.5694	70000	0.0959
1.5806	70500	0.0922
1.5918	71000	0.0898
1.6030	71500	0.0823
1.6142	72000	0.088
1.6254	72500	0.0832
1.6366	73000	0.0985
1.6478	73500	0.0944
1.6590	74000	0.0931
1.6703	74500	0.0808
1.6815	75000	0.0877
1.6927	75500	0.0746
1.7039	76000	0.0842
1.7151	76500	0.088
1.7263	77000	0.0792
1.7375	77500	0.0718
1.7487	78000	0.0941
1.7599	78500	0.0843
1.7711	79000	0.0835
1.7824	79500	0.0878
1.7936	80000	0.0771
1.8048	80500	0.0829
1.8160	81000	0.086
1.8272	81500	0.0802
1.8384	82000	0.0901
1.8496	82500	0.0859
1.8608	83000	0.0871
1.8720	83500	0.0787
1.8832	84000	0.0894
1.8944	84500	0.0895
1.9057	85000	0.0912
1.9169	85500	0.0795
1.9281	86000	0.0775
1.9393	86500	0.0693
1.9505	87000	0.0811
1.9617	87500	0.076
1.9729	88000	0.085
1.9841	88500	0.0904
1.9953	89000	0.087
2.0065	89500	0.061
2.0178	90000	0.0628
2.0290	90500	0.0721
2.0402	91000	0.0694
2.0514	91500	0.0618
2.0626	92000	0.0598
2.0738	92500	0.0701
2.0850	93000	0.0724
2.0962	93500	0.0623
2.1074	94000	0.0647
2.1186	94500	0.0643
2.1299	95000	0.066
2.1411	95500	0.0653
2.1523	96000	0.0648
2.1635	96500	0.0616
2.1747	97000	0.0661
2.1859	97500	0.0678
2.1971	98000	0.0621
2.2083	98500	0.0699
2.2195	99000	0.0631
2.2307	99500	0.0701
2.2420	100000	0.0663
2.2532	100500	0.0559
2.2644	101000	0.0667
2.2756	101500	0.0695
2.2868	102000	0.0655
2.2980	102500	0.0668
2.3092	103000	0.0661
2.3204	103500	0.0638
2.3316	104000	0.0686
2.3428	104500	0.0628
2.3540	105000	0.0649
2.3653	105500	0.0603
2.3765	106000	0.064
2.3877	106500	0.0651
2.3989	107000	0.0589
2.4101	107500	0.0621
2.4213	108000	0.061
2.4325	108500	0.068
2.4437	109000	0.0545
2.4549	109500	0.0691
2.4661	110000	0.0614
2.4774	110500	0.0661
2.4886	111000	0.0701
2.4998	111500	0.0549
2.5110	112000	0.0676
2.5222	112500	0.0599
2.5334	113000	0.0605
2.5446	113500	0.0671
2.5558	114000	0.0681
2.5670	114500	0.063
2.5782	115000	0.0586
2.5895	115500	0.0629
2.6007	116000	0.0586
2.6119	116500	0.0668
2.6231	117000	0.0606
2.6343	117500	0.0521
2.6455	118000	0.0619
2.6567	118500	0.065
2.6679	119000	0.052
2.6791	119500	0.0628
2.6903	120000	0.0642
2.7016	120500	0.0614
2.7128	121000	0.0663
2.7240	121500	0.0569
2.7352	122000	0.0648
2.7464	122500	0.0616
2.7576	123000	0.0536
2.7688	123500	0.0669
2.7800	124000	0.0612
2.7912	124500	0.0555
2.8024	125000	0.059
2.8136	125500	0.0549
2.8249	126000	0.0563
2.8361	126500	0.0616
2.8473	127000	0.06
2.8585	127500	0.0606
2.8697	128000	0.063
2.8809	128500	0.0572
2.8921	129000	0.0697
2.9033	129500	0.0561
2.9145	130000	0.065
2.9257	130500	0.0525
2.9370	131000	0.0597
2.9482	131500	0.0604
2.9594	132000	0.0534
2.9706	132500	0.0553
2.9818	133000	0.0593
2.9930	133500	0.0554

Framework Versions

• Python: 3.12.6
• Sentence Transformers: 3.4.1
• Transformers: 4.49.0
• PyTorch: 2.5.1+cu121
• Accelerate: 1.5.1
• Datasets: 3.4.0
• Tokenizers: 0.21.0

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",
}

MultipleNegativesRankingLoss

@misc{henderson2017efficient,
    title={Efficient Natural Language Response Suggestion for Smart Reply},
    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
    year={2017},
    eprint={1705.00652},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}