all-MiniLM-L6-v71-pair_score

all-MiniLM-L6-v71-pair_score

This is a sentence-transformers model finetuned from KhaledReda/all-MiniLM-L6-v69-pair_score on the pairs_with_scores_v57 dataset. It maps sentences & paragraphs to a 384-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: KhaledReda/all-MiniLM-L6-v69-pair_score <!-- at revision 6c1e2125fcfde82115870ab11c60c137f6d3db36 -->
• Maximum Sequence Length: 256 tokens
• Output Dimensionality: 384 dimensions
• Similarity Function: Cosine Similarity
• Training Dataset:
  • pairs_with_scores_v57
• Language: en
• License: apache-2.0

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': 256, 'do_lower_case': False, 'architecture': '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()
)

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 = [
    'silk case',
    'pillow case satin pillowcase hair fall prevention hair breakage reduction satin pillowcase moisture locking satin pillowcase for hair and skin did you know that sleeping on satin prevents hair fall up your hair and skin game now feel more glamorous with our satin pillowcase it reduces hair breakage locks moisture in your skin and hair and also has a cooling effect during the hot days.',
    'moussaka tajine moussaka',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[ 1.0000,  0.8193, -0.1220],
#         [ 0.8193,  1.0000, -0.1540],
#         [-0.1220, -0.1540,  1.0000]])

<!-- ### Direct Usage (Transformers) <details><summary>Click to see the direct usage in Transformers</summary> </details> --> <!-- ### Downstream Usage (Sentence Transformers) You can finetune this model on your own dataset. <details><summary>Click to expand</summary> </details> --> <!-- ### Out-of-Scope Use *List how the model may foreseeably be misused and address what users ought not to do with the model.* --> <!-- ## Bias, Risks and Limitations *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* --> <!-- ### Recommendations *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* -->

Training Details

Training Dataset

pairs_with_scores_v57

• Dataset: pairs_with_scores_v57 at 747d95d
• Size: 30,754,523 training samples
• Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
• Approximate statistics based on the first 1000 samples:

  	sentence1	sentence2	score
  type	string	string	float
  details	<ul><li>min: 3 tokens</li><li>mean: 7.45 tokens</li><li>max: 22 tokens</li></ul>	<ul><li>min: 4 tokens</li><li>mean: 58.24 tokens</li><li>max: 256 tokens</li></ul>	<ul><li>min: 0.0</li><li>mean: 0.21</li><li>max: 1.0</li></ul>

• Samples:

  sentence1	sentence2	score
  <code>macbook pro 14 case</code>	<code>yoga straps with additional leg adjustments additional leg straps adjustments leg straps</code>	<code>0.0</code>
  <code>borcam square stove dish with stand - 31.8 x 28.3cm</code>	<code>pasabahce zest microwave steamer - blue 1.84l how to use simply pour water into the glass base container place the steamer insert add the food you want to steam cover it with the silicon vented lid then place it in the microwave for as long as you need. pa-59524-61-7090</code>	<code>1.0</code>
  <code>plant</code>	<code>Plant Pot</code>	<code>1.0</code>

• Loss: <code>CoSENTLoss</code> with these parameters:

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

Evaluation Dataset

pairs_with_scores_v57

• Dataset: pairs_with_scores_v57 at 747d95d
• Size: 154,546 evaluation samples
• Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
• Approximate statistics based on the first 1000 samples:

  	sentence1	sentence2	score
  type	string	string	float
  details	<ul><li>min: 3 tokens</li><li>mean: 7.53 tokens</li><li>max: 22 tokens</li></ul>	<ul><li>min: 4 tokens</li><li>mean: 55.64 tokens</li><li>max: 256 tokens</li></ul>	<ul><li>min: 0.0</li><li>mean: 0.2</li><li>max: 1.0</li></ul>

• Samples:

  sentence1	sentence2	score
  <code>aha cream exfoliate pixi dust</code>	<code>the blueprint hand cream unisex hand cream blue hand cream vitamin e hand cream blueprint cream unisex skyblue. 14.7 x 3.8 x 3.2 cm. highlight ingredients. panthenol this chemical substance works wonders in protecting your skin against uv radiation due to its anti-inflammatory properties. it helps in skin redness and irritation providing the skin with thorough hydration and moisturization. cocoa butter the cocoa bean extract known for its remarkable moisturizing properties has high percentages of fat and antioxidants that act as a protective barrier for the skin against uv radiation and aging by preserving moisture and improving blood flow to the skin. additionally it is helpful for sensitive skin and moderating stretch marks. vitamin e an important nutrient that helps in prote</code>	<code>0.0</code>
  <code>the rastau fountain fringed dress</code>	<code>straight linen dress with ribbons women dress ribbons dress straight dress</code>	<code>1.0</code>
  <code>loose oversized hoodie tea</code>	<code>adult karate uniform 500 cotton karate uniform japanese karate uniform training karate uniform short sleeves karate uniform boxing karate uniform our team of passionate practitioners designed this uniform for your karate combat techniques and katas in training sessions several times a week. we ve developed a 100 cotton uniform with a japanese look for your katas and combat techniques. its short sleeves and striking fabric help to highlight your movements</code>	<code>0.0</code>

• Loss: <code>CoSENTLoss</code> with these parameters:

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

Training Hyperparameters

Non-Default Hyperparameters

• eval_strategy: steps
• per_device_train_batch_size: 64
• per_device_eval_batch_size: 64
• learning_rate: 2e-05
• num_train_epochs: 1
• warmup_ratio: 0.1
• fp16: True

All Hyperparameters

<details><summary>Click to expand</summary>

• 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: 64
• 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: 2e-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: 1
• max_steps: -1
• lr_scheduler_type: linear
• lr_scheduler_kwargs: {}
• warmup_ratio: 0.1
• 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
• hub_revision: None
• 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
• liger_kernel_config: None
• eval_use_gather_object: False
• average_tokens_across_devices: False
• prompts: None
• batch_sampler: batch_sampler
• multi_dataset_batch_sampler: proportional
• router_mapping: {}
• learning_rate_mapping: {}

</details>

Training Logs

<details><summary>Click to expand</summary>

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