SentenceTransformer based on intfloat/e5-base
This is a sentence-transformers model finetuned from intfloat/e5-base on the train dataset. 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: intfloat/e5-base
- Maximum Sequence Length: 512 tokens
- Output Dimensionality: 768 tokens
- Similarity Function: Cosine Similarity
- Training Dataset:
- train
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: BertModel
(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})
(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("sentence_transformers_model_id")
# Run inference
sentences = [
'Essential Soft-skills & Competencies for Today’s Executives & Employees\nSoft-skills are defined as the personal attributes that enhance and individual’s interaction, job performance and career prospect. Soft-skills are interpersonal and generally applicable.\n- Excellent presentation skill.\n- Emotional intelligence.\n- Social/networking skill,\n- Business writing/business communication skills.\n- Effective communication skill (fluent speaking and good writing).\n- Work/workplace etiquette.\n- Team playing – the ability to work productively and effectively with others. Also, inter-personal skill.\n- Information and communication technology/computer skills.\n- Apt to learning – ability to learn and adapt to a job very fast with minimal supervision and explanation. Being clever and resourceful.\n- Teaching/training/mentoring skills and abilities.\n- Numerical/mathematical skill.\n- Information gathering and acquisition skills. Also, the ability to keep tabs of happenings in your business, corporate, socio-economic and political environment.\n- Anticipation skill – always try to see things/events coming before they actually arrived/happen.\n- Leadership, supervisory and managerial skills.\n- Adaptability to organisational/corporate changes (both role changes and management changes. Also, change management or transition management.\n- Initiative & independent skills/innovation & idea generation skills.\n- Financial resources management skill.\n- Human resources development and management skill.\n- Conflict resolution and conflict management skills.\n- Motivational skill – ability to motivate your team/group/workers. Also, self-motivation and management skills.\n- Flexibility and creativity. Also, creative thinking skill.\n- Risk taking, risk bearing and risk management skills.\n- Financial report/financial statement interpretation and analysing skills.\nNote that, today’s workplace requires that employees, managers and executives to possess these essential soft-skills…\nLike our page: facebook.com/vaxity',
'Skills',
'AMP',
]
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
train
- Dataset: train
- Size: 100,000 training samples
- Columns:
anchor
andpositive
- Approximate statistics based on the first 1000 samples:
anchor positive type string string details - min: 51 tokens
- mean: 386.54 tokens
- max: 512 tokens
- min: 3 tokens
- mean: 3.46 tokens
- max: 7 tokens
- Samples:
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Both companies are part of Lloyds Banking Group plc. See Company Information. Whilst our services will be available to many customers, there are countries where, due to legal or regulatory restraints, we cannot provide them. Rules and regulations made under the UK Financial Services and Markets Act for the protection of depositors and investors, including the Financial Services Compensation Scheme, do not apply to the financial services business of companies within the Lloyds Banking Group carried out from offices outside of the United Kingdom.
Lloyds Bank Corporate Markets plc. Registered in England and Wales no. Authorisation can be checked on the Financial Services Register. Services provided by the Jersey, Guernsey and Isle of Man branches of Lloyds Bank Corporate Markets plc will be subject to the regulatory regime applicable in their respective jurisdiction, which will differ in some or all respects from that of the UK.
Further information about the regulatory status of the Jersey, Guernsey and Isle of Man branches of Lloyds Bank Corporate Markets plc, is set out below. Lloyds Banking Group plc does not guarantee the liabilities of its subsidiaries.
Rules and regulations made under the UK Financial Services and Markets Act , including the Financial Services Compensation Scheme, do not apply to the financial services business of companies within the Lloyds Banking Group carried out from offices outside the United Kingdom. Copies of our terms and conditions and latest report and accounts are available upon request from the above registered office.
Copies of our terms and conditions are available on request from the above registered office. Eligible deposits are deposits held by private individuals and charities. Depositor protection does not extend to corporations, small to medium sized enterprises, partnerships and trusts. Full details of the Scheme and banking groups covered are available on the States of Jersey website or on request.
Deposits with the Guernsey branch of Lloyds Bank Corporate Markets plc will not be covered by any equivalent deposit compensation scheme to the Guernsey Banking Deposit Compensation Scheme in any other jurisdiction. Lloyds Bank International Services Limited. Please note that, in relation to banking services which we provide, you might not be eligible for compensation under a deposit protection guarantee scheme available in your country of residence.
If in doubt, contact your local banking regulator, visit their website or seek independent advice. Depositors may wish to form their own view on the financial standing of Lloyds Bank Corporate Markets plc and Lloyds Bank International Services Limited and their respective parent company based upon publicly available information. Legislation or regulations in your home jurisdiction may prohibit you from entering into such a transaction with us.
We reserve the right to make final determination on whether you are eligible for any products or services. Residents or Nationals of certain jurisdictions may be subject to exchange controls and should seek independent advice before entering into any transactions with us. Correspondent and recipient bank charges may apply. Find out how the process works.
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The recipient bank may still charge its customer a fee but we cannot provide information about such a fee. The amount of the Correspondent Bank Fee depends on the location of the recipient bank. Cheques in a foreign currency drawn in a different country e. We offer a wide range of foreign exchange services. When you make a payment online, you'll be told how much you should expect to leave your account or how much the recipient will receive.
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Payments made before 3pm on a working day will be processed the same day. Otherwise, the payment will be processed on the following working day. Mon-Fri 8am-6pm and Sat. Check eligibility Is international banking for you? Find out more International Current Accounts. Find out more Guide to sending money abroad. The Jersey, Guernsey and Isle of Man branches of Lloyds Bank Corporate Markets plc are each separately licensed as regulated deposit-takers in their respective jurisdictions.
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This service is only available to HSBC customers, but you could open an account with the bank in your home country and then ask for a transfer. Some links in this article may be affiliate links. If you click on them we may earn a small commission.
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- Loss:
GISTEmbedLoss
with these parameters:{'guide': SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 384, '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() ), 'temperature': 0.01}
Training Hyperparameters
All Hyperparameters
Click to expand
overwrite_output_dir
: Falsedo_predict
: Falseeval_strategy
: noprediction_loss_only
: Trueper_device_train_batch_size
: 8per_device_eval_batch_size
: 8per_gpu_train_batch_size
: Noneper_gpu_eval_batch_size
: Nonegradient_accumulation_steps
: 1eval_accumulation_steps
: Nonetorch_empty_cache_steps
: Nonelearning_rate
: 5e-05weight_decay
: 0.0adam_beta1
: 0.9adam_beta2
: 0.999adam_epsilon
: 1e-08max_grad_norm
: 1.0num_train_epochs
: 3.0max_steps
: -1lr_scheduler_type
: linearlr_scheduler_kwargs
: {}warmup_ratio
: 0.0warmup_steps
: 0log_level
: passivelog_level_replica
: warninglog_on_each_node
: Truelogging_nan_inf_filter
: Truesave_safetensors
: Truesave_on_each_node
: Falsesave_only_model
: Falserestore_callback_states_from_checkpoint
: Falseno_cuda
: Falseuse_cpu
: Falseuse_mps_device
: Falseseed
: 42data_seed
: Nonejit_mode_eval
: Falseuse_ipex
: Falsebf16
: Falsefp16
: Falsefp16_opt_level
: O1half_precision_backend
: autobf16_full_eval
: Falsefp16_full_eval
: Falsetf32
: Nonelocal_rank
: 0ddp_backend
: Nonetpu_num_cores
: Nonetpu_metrics_debug
: Falsedebug
: []dataloader_drop_last
: Falsedataloader_num_workers
: 0dataloader_prefetch_factor
: Nonepast_index
: -1disable_tqdm
: Falseremove_unused_columns
: Truelabel_names
: Noneload_best_model_at_end
: Falseignore_data_skip
: Falsefsdp
: []fsdp_min_num_params
: 0fsdp_config
: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}fsdp_transformer_layer_cls_to_wrap
: Noneaccelerator_config
: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}deepspeed
: Nonelabel_smoothing_factor
: 0.0optim
: adamw_torchoptim_args
: Noneadafactor
: Falsegroup_by_length
: Falselength_column_name
: lengthddp_find_unused_parameters
: Noneddp_bucket_cap_mb
: Noneddp_broadcast_buffers
: Falsedataloader_pin_memory
: Truedataloader_persistent_workers
: Falseskip_memory_metrics
: Trueuse_legacy_prediction_loop
: Falsepush_to_hub
: Falseresume_from_checkpoint
: Nonehub_model_id
: Nonehub_strategy
: every_savehub_private_repo
: Falsehub_always_push
: Falsegradient_checkpointing
: Falsegradient_checkpointing_kwargs
: Noneinclude_inputs_for_metrics
: Falseeval_do_concat_batches
: Truefp16_backend
: autopush_to_hub_model_id
: Nonepush_to_hub_organization
: Nonemp_parameters
:auto_find_batch_size
: Falsefull_determinism
: Falsetorchdynamo
: Noneray_scope
: lastddp_timeout
: 1800torch_compile
: Falsetorch_compile_backend
: Nonetorch_compile_mode
: Nonedispatch_batches
: Nonesplit_batches
: Noneinclude_tokens_per_second
: Falseinclude_num_input_tokens_seen
: Falseneftune_noise_alpha
: Noneoptim_target_modules
: Nonebatch_eval_metrics
: Falseeval_on_start
: Falseeval_use_gather_object
: Falsebatch_sampler
: batch_samplermulti_dataset_batch_sampler
: proportional
Training Logs
Epoch | Step | Training Loss |
---|---|---|
0.04 | 500 | 0.2671 |
0.08 | 1000 | 0.2567 |
0.12 | 1500 | 0.28 |
0.16 | 2000 | 0.2596 |
0.2 | 2500 | 0.2581 |
0.24 | 3000 | 0.2334 |
0.28 | 3500 | 0.2443 |
0.32 | 4000 | 0.2331 |
0.36 | 4500 | 0.2355 |
0.4 | 5000 | 0.2322 |
0.44 | 5500 | 0.2342 |
0.48 | 6000 | 0.2336 |
0.52 | 6500 | 0.2004 |
0.56 | 7000 | 0.1871 |
0.6 | 7500 | 0.2108 |
0.64 | 8000 | 0.2046 |
0.68 | 8500 | 0.2158 |
0.72 | 9000 | 0.2125 |
0.76 | 9500 | 0.2124 |
0.8 | 10000 | 0.2448 |
0.84 | 10500 | 0.2037 |
0.88 | 11000 | 0.1873 |
0.92 | 11500 | 0.2022 |
0.96 | 12000 | 0.2096 |
1.0 | 12500 | 0.2223 |
1.04 | 13000 | 0.1314 |
1.08 | 13500 | 0.1188 |
1.12 | 14000 | 0.1494 |
1.16 | 14500 | 0.1312 |
1.2 | 15000 | 0.1311 |
1.24 | 15500 | 0.1261 |
1.28 | 16000 | 0.1144 |
1.32 | 16500 | 0.1153 |
1.3600 | 17000 | 0.1189 |
1.4 | 17500 | 0.1067 |
1.44 | 18000 | 0.1236 |
1.48 | 18500 | 0.1163 |
1.52 | 19000 | 0.0926 |
1.56 | 19500 | 0.0946 |
1.6 | 20000 | 0.0987 |
1.6400 | 20500 | 0.1074 |
1.6800 | 21000 | 0.1075 |
1.72 | 21500 | 0.0897 |
1.76 | 22000 | 0.0988 |
1.8 | 22500 | 0.0997 |
1.8400 | 23000 | 0.0872 |
1.88 | 23500 | 0.0885 |
1.92 | 24000 | 0.0973 |
1.96 | 24500 | 0.1003 |
2.0 | 25000 | 0.0875 |
2.04 | 25500 | 0.0754 |
2.08 | 26000 | 0.0716 |
2.12 | 26500 | 0.0771 |
2.16 | 27000 | 0.0733 |
2.2 | 27500 | 0.0748 |
2.24 | 28000 | 0.0834 |
2.2800 | 28500 | 0.0792 |
2.32 | 29000 | 0.0662 |
2.36 | 29500 | 0.08 |
2.4 | 30000 | 0.0572 |
2.44 | 30500 | 0.0723 |
2.48 | 31000 | 0.0641 |
2.52 | 31500 | 0.0555 |
2.56 | 32000 | 0.0641 |
2.6 | 32500 | 0.0597 |
2.64 | 33000 | 0.061 |
2.68 | 33500 | 0.0647 |
2.7200 | 34000 | 0.0537 |
2.76 | 34500 | 0.0614 |
2.8 | 35000 | 0.0595 |
2.84 | 35500 | 0.0528 |
2.88 | 36000 | 0.0623 |
2.92 | 36500 | 0.0595 |
2.96 | 37000 | 0.0598 |
3.0 | 37500 | 0.0626 |
Framework Versions
- Python: 3.10.4
- Sentence Transformers: 3.0.1
- Transformers: 4.44.0
- PyTorch: 2.4.0+cu121
- Accelerate: 0.34.2
- Datasets: 2.21.0
- Tokenizers: 0.19.1
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}
}
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