Edit model card

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

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 and positive
  • 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:
    anchor positive
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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: False
  • do_predict: False
  • eval_strategy: no
  • prediction_loss_only: True
  • per_device_train_batch_size: 8
  • per_device_eval_batch_size: 8
  • 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: 1.0
  • num_train_epochs: 3.0
  • 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: False
  • 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: False
  • hub_always_push: False
  • gradient_checkpointing: False
  • gradient_checkpointing_kwargs: None
  • include_inputs_for_metrics: False
  • 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
  • eval_use_gather_object: False
  • batch_sampler: batch_sampler
  • multi_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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