Pascalymb/result_model

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 <!-- at revision 284169e2c18b482372374a251b8dc1e1756416de -->
• —Maximum Sequence Length: 512 tokens
• —Output Dimensionality: 1024 dimensions
• —Similarity Function: Cosine Similarity
• —Supported Modality: Text
• —Training Dataset:
• —all-nli <!-- - Language: Unknown --> <!-- - License: Unknown -->

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 = [
    'Seven people are wading in a natural pool.',
    'there were five people',
    'The man is riding a unicycle.',
]
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, 0.7814, 0.6510],
#         [0.7814, 1.0000, 0.7047],
#         [0.6510, 0.7047, 1.0000]])

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Downstream Usage (Sentence Transformers)

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Evaluation

Metrics

Semantic Similarity

• —Dataset: pair-score-evaluator-dev
• —Evaluated with <code>EmbeddingSimilarityEvaluator</code>

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Training Details

Training Dataset

all-nli

• —Dataset: all-nli
• —Size: 16,000 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: 7 tokens</li><li>mean: 18.91 tokens</li><li>max: 66 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 11.42 tokens</li><li>max: 36 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.53</li><li>max: 1.0</li></ul> |
• —Samples: | sentence1 | sentence2 | score | |:---------------------------------------------------------------------------------------------|:------------------------------------------------------|:-----------------| | <code>A man in a white shirt is on a rooftop lifting a board.</code> | <code>A man has a white shirt.</code> | <code>1.0</code> | | <code>Two men in shorts and sandals are carrying food and drinks at a farmers market.</code> | <code>Men shopping their local market.</code> | <code>1.0</code> | | <code>A woman with her head down is in a very run down area.</code> | <code>the man in a suit talks on his cellphone</code> | <code>0.0</code> |
• —Loss: <code>CoSENTLoss</code> with these parameters:

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

Evaluation Dataset

all-nli

• —Dataset: all-nli
• —Size: 4,000 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: 7 tokens</li><li>mean: 19.28 tokens</li><li>max: 70 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 11.46 tokens</li><li>max: 46 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.49</li><li>max: 1.0</li></ul> |
• —Samples: | sentence1 | sentence2 | score | |:---------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------|:-----------------| | <code>A person dressed in natural clothing taking their picture in a mirror.</code> | <code>the person is nude at the bay</code> | <code>0.0</code> | | <code>A basketball team of 8 girls is doing a hand huddle.</code> | <code>An all girls basketball team gets ready to start a game.</code> | <code>1.0</code> | | <code>A person in a tan and blue sweater hanging clothes on a clothesline outside the window of her building.</code> | <code>There are no clothes.</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

• —per_device_eval_batch_size: 16
• —gradient_accumulation_steps: 4
• —learning_rate: 2e-05
• —num_train_epochs: 4
• —warmup_steps: 0.05
• —bf16: True
• —bf16_full_eval: True
• —dataloader_num_workers: 4
• —load_best_model_at_end: True
• —push_to_hub: True
• —gradient_checkpointing: True

All Hyperparameters

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

• —do_predict: False
• —prediction_loss_only: True
• —per_device_train_batch_size: 8
• —per_device_eval_batch_size: 16
• —gradient_accumulation_steps: 4
• —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: 4
• —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: True
• —fp16_full_eval: False
• —tf32: None
• —local_rank: -1
• —ddp_backend: None
• —debug: []
• —dataloader_drop_last: False
• —dataloader_num_workers: 4
• —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: {'minnumparams': 0, 'xla': False, 'xlafsdpv2': False, 'xlafsdpgrad_ckpt': False}
• —accelerator_config: {'splitbatches': False, 'dispatchbatches': None, 'evenbatches': True, 'useseedablesampler': True, 'nonblocking': False, 'gradientaccumulationkwargs': None}
• —parallelism_config: None
• —deepspeed: None
• —label_smoothing_factor: 0.0
• —optim: adamwtorchfused
• —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: {}

</details>

Training Logs

• —The bold row denotes the saved checkpoint.

Training Time

• —Training: 1.6 hours

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}
}

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