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 woman wearing all white and eating, walks next to a man holding a
      briefcase.
    sentences:
      - A married couple is walking next to each other.
      - The women do not care what clothes they wear.
      - The diners are at a restaurant.
  - 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:
      - A person is training his horse for a competition.
      - >-
        The friends have just met for the first time in 20 years, and have had a
        great time catching up.
      - >-
        Two adults run across the street to get away from a red shirted person
        chasing them.
  - source_sentence: A boy is jumping on skateboard in the middle of a red bridge.
    sentences:
      - The boy is wearing safety equipment.
      - >-
        A woman eats ice cream walking down the sidewalk, and there is another
        woman in front of her with a purse.
      - The woman is cold.
  - source_sentence: >-
      High fashion ladies wait outside a tram beside a crowd of people in the
      city.
    sentences:
      - The women enjoy having a good fashion sense.
      - A woman in white.
      - Olympic swimming.
  - source_sentence: Two women, holding food carryout containers, hug.
    sentences:
      - The people are related.
      - A man and a woman walk down a crowded city street.
      - >-
        The two sisters saw each other across the crowded diner and shared a
        hug, both clutching their doggie bags.
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: -0.35822503702342545
            name: Pearson Cosine
          - type: spearman_cosine
            value: -0.4145870047803723
            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 women, holding food carryout containers, hug.',
    'The two sisters saw each other across the crowded diner and shared a hug, both clutching their doggie bags.',
    'The people are related.',
]
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, 0.9138, 0.9157],
#         [0.9138, 1.0000, 0.9143],
#         [0.9157, 0.9143, 1.0000]])

Evaluation

Metrics

Semantic Similarity

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

Metric	Value
pearson_cosine	-0.3582
spearman_cosine	-0.4146

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: 25.79 tokens max: 52 tokens	min: 6 tokens mean: 11.45 tokens max: 25 tokens	min: 0.0 mean: 0.52 max: 1.0

• Samples:

  sentence1	sentence2	score
  A Little League team tries to catch a runner sliding into a base in an afternoon game.	A team is trying to tag a runner out.	1.0
  Woman in white in foreground and a man slightly behind walking with a sign for John's Pizza and Gyro in the background.	The woman and man are outdoors.	1.0
  A couple play in the tide with their young son.	The family is sitting down for dinner.	0.0

• 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: 14 tokens mean: 25.5 tokens max: 52 tokens	min: 5 tokens mean: 14.1 tokens max: 29 tokens	min: 0.0 mean: 0.45 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.	The woman is wearing white.	1.0
  High fashion ladies wait outside a tram beside a crowd of people in the city.	The women enjoy having a good fashion sense.	0.5
  An older man is drinking orange juice at a restaurant.	Two women are at a restaurant drinking wine.	0.0

• 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	2.7118	-	-
0.5	5	4.8898	-	-
1.0	10	3.096	2.7557	-0.2163
0.1	1	2.4172	-	-
0.5	5	2.9285	-	-
1.0	10	2.8121	3.2682	-0.4146

• The bold row denotes the saved checkpoint.

Training Time

• Training: 3.7 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}
}