README.md

metadata

tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:80
  - loss:CoSENTLoss
base_model: abdeljalilELmajjodi/model
widget:
  - source_sentence: A couple play in the tide with their young son.
    sentences:
      - The family is outside.
      - A man in a restaurant is waiting for his meal to arrive.
      - Two adults walking across a road
  - source_sentence: A couple playing with a little boy on the beach.
    sentences:
      - They are working for John's Pizza.
      - The woman is wearing green.
      - A couple are playing frisbee with a young child at the beach.
  - source_sentence: A man, woman, and child enjoying themselves on a beach.
    sentences:
      - A family of three is at the mall shopping.
      - A person is training his horse for a competition.
      - The diners are at a restaurant.
  - source_sentence: Two women who just had lunch hugging and saying goodbye.
    sentences:
      - >-
        The friends have just met for the first time in 20 years, and have had a
        great time catching up.
      - A blond man drinking water from a fountain.
      - The woman and man are outdoors.
  - source_sentence: >-
      Two adults, one female in white, with shades and one male, gray clothes,
      walking across a street, away from a eatery with a blurred image of a dark
      colored red shirted person in the foreground.
    sentences:
      - The man with the sign is caucasian.
      - A woman in white.
      - The adults are both male and female.
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
  - pearson_cosine
  - spearman_cosine
model-index:
  - name: SentenceTransformer based on abdeljalilELmajjodi/model
    results:
      - task:
          type: semantic-similarity
          name: Semantic Similarity
        dataset:
          name: pair score evaluator dev
          type: pair-score-evaluator-dev
        metrics:
          - type: pearson_cosine
            value: -0.06308677482270299
            name: Pearson Cosine
          - type: spearman_cosine
            value: -0.1064194728626353
            name: Spearman Cosine

SentenceTransformer based on abdeljalilELmajjodi/model

This is a sentence-transformers model finetuned from abdeljalilELmajjodi/model on the all-nli dataset. It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for retrieval.

Model Details

Model Description

• Model Type: Sentence Transformer
• Base model: abdeljalilELmajjodi/model
• Maximum Sequence Length: 512 tokens
• Output Dimensionality: 1024 dimensions
• Similarity Function: Cosine Similarity
• Supported Modality: Text
• Training Dataset:
  • all-nli

Model Sources

• Documentation: Sentence Transformers Documentation
• Repository: Sentence Transformers on GitHub
• Hugging Face: Sentence Transformers on Hugging Face

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'transformer_task': 'feature-extraction', 'modality_config': {'text': {'method': 'forward', 'method_output_name': 'last_hidden_state'}}, 'module_output_name': 'token_embeddings', 'architecture': 'XLMRobertaModel'})
  (1): Pooling({'embedding_dimension': 1024, 'pooling_mode': 'mean', '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 = [
    'Two adults, one female in white, with shades and one male, gray clothes, walking across a street, away from a eatery with a blurred image of a dark colored red shirted person in the foreground.',
    'The adults are both male and female.',
    'The man with the sign is caucasian.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.9915, 0.9927],
#         [0.9915, 1.0000, 0.9914],
#         [0.9927, 0.9914, 1.0000]])

Evaluation

Metrics

Semantic Similarity

• Dataset: pair-score-evaluator-dev
• Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	-0.0631
spearman_cosine	-0.1064

Training Details

Training Dataset

all-nli

• Dataset: all-nli
• Size: 80 training samples
• Columns: sentence1, sentence2, and score
• Approximate statistics based on the first 80 samples:

  	sentence1	sentence2	score
  type	string	string	float
  details	min: 10 tokens mean: 25.66 tokens max: 52 tokens	min: 5 tokens mean: 11.88 tokens max: 29 tokens	min: 0.0 mean: 0.47 max: 1.0

• Samples:

  sentence1	sentence2	score
  Two adults, one female in white, with shades and one male, gray clothes, walking across a street, away from a eatery with a blurred image of a dark colored red shirted person in the foreground.	Some people board a train.	0.0
  A woman is walking across the street eating a banana, while a man is following with his briefcase.	A person eating.	1.0
  A woman is walking across the street eating a banana, while a man is following with his briefcase.	The mans briefcase is for work.	0.5

• Loss: CoSENTLoss with these parameters:

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

Evaluation Dataset

all-nli

• Dataset: all-nli
• Size: 20 evaluation samples
• Columns: sentence1, sentence2, and score
• Approximate statistics based on the first 20 samples:

  	sentence1	sentence2	score
  type	string	string	float
  details	min: 13 tokens mean: 26.0 tokens max: 52 tokens	min: 7 tokens mean: 12.4 tokens max: 25 tokens	min: 0.0 mean: 0.65 max: 1.0

• Samples:

  sentence1	sentence2	score
  Woman in white in foreground and a man slightly behind walking with a sign for John's Pizza and Gyro in the background.	They are working for John's Pizza.	0.5
  Two women who just had lunch hugging and saying goodbye.	The friends have just met for the first time in 20 years, and have had a great time catching up.	0.5
  A person on a horse jumps over a broken down airplane.	A person is training his horse for a competition.	0.5

• Loss: CoSENTLoss with these parameters:

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

Training Hyperparameters

Non-Default Hyperparameters

• num_train_epochs: 1
• warmup_steps: 0.05
• bf16: True
• fp16_full_eval: True
• load_best_model_at_end: True
• push_to_hub: True
• gradient_checkpointing: True

All Hyperparameters

Click to expand

• do_predict: False
• prediction_loss_only: True
• per_device_train_batch_size: 8
• per_device_eval_batch_size: 8
• 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: 1
• max_steps: -1
• lr_scheduler_type: linear
• lr_scheduler_kwargs: None
• warmup_ratio: None
• warmup_steps: 0.05
• log_level: passive
• log_level_replica: warning
• log_on_each_node: True
• logging_nan_inf_filter: True
• enable_jit_checkpoint: False
• save_on_each_node: False
• save_only_model: False
• restore_callback_states_from_checkpoint: False
• use_cpu: False
• seed: 42
• data_seed: None
• bf16: True
• fp16: False
• bf16_full_eval: False
• fp16_full_eval: True
• tf32: None
• local_rank: -1
• ddp_backend: None
• debug: []
• dataloader_drop_last: False
• dataloader_num_workers: 0
• dataloader_prefetch_factor: None
• disable_tqdm: False
• remove_unused_columns: True
• label_names: None
• load_best_model_at_end: True
• ignore_data_skip: False
• fsdp: []
• fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
• accelerator_config: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
• parallelism_config: None
• deepspeed: None
• label_smoothing_factor: 0.0
• optim: adamw_torch_fused
• optim_args: None
• group_by_length: False
• length_column_name: length
• project: huggingface
• trackio_space_id: trackio
• 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
• push_to_hub: True
• 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: True
• gradient_checkpointing_kwargs: None
• include_for_metrics: []
• eval_do_concat_batches: True
• auto_find_batch_size: False
• full_determinism: False
• ddp_timeout: 1800
• torch_compile: False
• torch_compile_backend: None
• torch_compile_mode: None
• include_num_input_tokens_seen: no
• 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: True
• use_cache: False
• prompts: None
• batch_sampler: batch_sampler
• multi_dataset_batch_sampler: proportional
• router_mapping: {}
• learning_rate_mapping: {}

Training Logs

Epoch	Step	Training Loss	Validation Loss	pair-score-evaluator-dev_spearman_cosine
0.1	1	2.7818	-	-
0.5	5	2.8166	-	-
1.0	10	3.1101	2.7679	-0.1064

• The bold row denotes the saved checkpoint.

Training Time

• Training: 8.5 minutes

Framework Versions

• Python: 3.12.13
• Sentence Transformers: 5.4.1
• Transformers: 5.0.0
• PyTorch: 2.10.0+cu128
• Accelerate: 1.13.0
• Datasets: 4.8.5
• Tokenizers: 0.22.2

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",
}

CoSENTLoss

@article{10531646,
    author={Huang, Xiang and Peng, Hao and Zou, Dongcheng and Liu, Zhiwei and Li, Jianxin and Liu, Kay and Wu, Jia and Su, Jianlin and Yu, Philip S.},
    journal={IEEE/ACM Transactions on Audio, Speech, and Language Processing},
    title={CoSENT: Consistent Sentence Embedding via Similarity Ranking},
    year={2024},
    doi={10.1109/TASLP.2024.3402087}
}