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csolaina
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lora_r4_a16_d5_bs16_ep1

Sentence Similarity
sentence-transformers
Safetensors
feature-extraction
Generated from Trainer
dataset_size:13734
loss:MultipleNegativesRankingLoss
Model card Files Community

SentenceTransformer based on intfloat/e5-small-v2

This is a sentence-transformers model finetuned from intfloat/e5-small-v2. 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: intfloat/e5-small-v2
  • Maximum Sequence Length: 512 tokens
  • Output Dimensionality: 384 dimensions
  • Similarity Function: Cosine Similarity

Model Sources

Full Model Architecture 

SentenceTransformer(
  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: PeftModelForFeatureExtraction 
  (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 = [
    'Evaluate predicted target values for X relative to y_true',
    '    def __call__(self, estimator, X, y_true, sample_weight=None, **kwargs):\n        """Evaluate predicted target values for X relative to y_true.\n\n        Parameters\n        ----------\n        estimator : object\n            Trained estimator to use for scoring. Must have a predict_proba\n            method; the output of that is used to compute the score.\n\n        X : {array-like, sparse matrix}\n            Test data that will be fed to estimator.predict.\n\n        y_true : array-like\n            Gold standard target values for X.\n\n        sample_weight : array-like of shape (n_samples,), default=None\n            Sample weights.\n\n        **kwargs : dict\n            Other parameters passed to the scorer. Refer to\n            :func:`set_score_request` for more details.\n\n            Only available if `enable_metadata_routing=True`. See the\n            :ref:`User Guide <metadata_routing>`.\n\n            .. versionadded:: 1.3\n\n        Returns\n        -------\n        score : float\n            Score function applied to prediction of estimator on X.\n        """\n        # TODO (1.8): remove in 1.8 (scoring="max_error" has been deprecated in 1.6)\n        if self._deprecation_msg is not None:\n            warnings.warn(\n                self._deprecation_msg, category=DeprecationWarning, stacklevel=2\n            )\n\n        _raise_for_params(kwargs, self, None)\n\n        _kwargs = copy.deepcopy(kwargs)\n        if sample_weight is not None:\n            _kwargs["sample_weight"] = sample_weight\n\n        return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)',
    'def test_hdbscan_usable_inputs(X, kwargs):\n    """\n    Tests that HDBSCAN works correctly for array-likes and precomputed inputs\n    with non-finite points.\n    """\n    HDBSCAN(min_samples=1, **kwargs).fit(X)',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]

Training Details

Training Dataset

Unnamed Dataset

  • Size: 13,734 training samples
  • Columns: sentence_0 and sentence_1
  • Approximate statistics based on the first 1000 samples:
    sentence_0 sentence_1
    type string string
    details
    • min: 3 tokens
    • mean: 8.78 tokens
    • max: 63 tokens
    • min: 9 tokens
    • mean: 233.15 tokens
    • max: 512 tokens
  • Samples:
    sentence_0 sentence_1
    Get the estimator def get_estimator(self):
    """Get the estimator.

    Returns
    -------
    estimator     : estimator object
    The cloned estimator object.
    """
    # TODO(1.8): remove and only keep clone(self.estimator)
    if self.estimator is None and self.base_estimator != "deprecated":
    estimator_ = clone(self.base_estimator)

    warn(
    (
    "base_estimator has been deprecated in 1.6 and will be removed"
    " in 1.8. Please use estimator instead."
    ),
    FutureWarning,
    )
    # TODO(1.8) remove
    elif self.estimator is None and self.base_estimator == "deprecated":
    raise ValueError(
    "You must pass an estimator to SelfTrainingClassifier. Use estimator."
    )
    elif self.estimator is not None and self.base_estimator != "deprecated":
    raise ValueError(
    "You must p...
    Gaussian Naive Bayes (GaussianNB) class GaussianNB(BaseNB):
    """
    Gaussian Naive Bayes (GaussianNB).

    Can perform online updates to model parameters via :meth:partial_fit.
    For details on algorithm used to update feature means and variance online,
    see Stanford CS tech report STAN-CS-79-773 by Chan, Golub, and LeVeque<br> <http://i.stanford.edu/pub/cstr/reports/cs/tr/79/773/CS-TR-79-773.pdf>    .

    Read more in the :ref:User Guide <gaussian_naive_bayes>.

    Parameters
    ----------
    priors : array-like of shape (n_classes,), default=None
    Prior probabilities of the classes. If specified, the priors are not
    adjusted according to the data.

    var_smoothing : float, default=1e-9
    Portion of the largest variance of all features that is added to
    variances for calculation stability.

    .. versionadded:: 0.20

    Attributes
    ----------
    class_count_ : ndarray of shape (n_classes,)
    number of training samples observed in each class.

    class_pri...
    test rfe cv n jobs def test_rfe_cv_n_jobs(global_random_seed):
    generator = check_random_state(global_random_seed)
    iris = load_iris()
    X = np.c_[iris.data, generator.normal(size=(len(iris.data), 6))]
    y = iris.target

    rfecv = RFECV(estimator=SVC(kernel="linear"))
    rfecv.fit(X, y)
    rfecv_ranking = rfecv.ranking_

    rfecv_cv_results_ = rfecv.cv_results_

    rfecv.set_params(n_jobs=2)
    rfecv.fit(X, y)
    assert_array_almost_equal(rfecv.ranking_, rfecv_ranking)

    assert rfecv_cv_results_.keys() == rfecv.cv_results_.keys()
    for key in rfecv_cv_results_.keys():
    assert rfecv_cv_results_[key] == pytest.approx(rfecv.cv_results_[key])
  • Loss: MultipleNegativesRankingLoss with these parameters:
    {
    "scale": 20.0,
    "similarity_fct": "cos_sim"
}

Training Hyperparameters

Non-Default Hyperparameters

  • per_device_train_batch_size: 16
  • per_device_eval_batch_size: 16
  • num_train_epochs: 1
  • fp16: True
  • multi_dataset_batch_sampler: round_robin

All Hyperparameters

Click to expand
  • overwrite_output_dir: False
  • do_predict: False
  • eval_strategy: no
  • prediction_loss_only: True
  • per_device_train_batch_size: 16
  • per_device_eval_batch_size: 16
  • 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
  • num_train_epochs: 1
  • 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: 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}
  • tp_size: 0
  • 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
  • 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
  • eval_use_gather_object: False
  • average_tokens_across_devices: False
  • prompts: None
  • batch_sampler: batch_sampler
  • multi_dataset_batch_sampler: round_robin

Training Logs

Epoch Step Training Loss
0.5821 500 0.6129

Framework Versions

  • Python: 3.11.12
  • Sentence Transformers: 3.4.1
  • Transformers: 4.51.3
  • PyTorch: 2.6.0+cu124
  • Accelerate: 1.6.0
  • Datasets: 3.5.1
  • Tokenizers: 0.21.1

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",
}

MultipleNegativesRankingLoss 

@misc{henderson2017efficient,
    title={Efficient Natural Language Response Suggestion for Smart Reply},
    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
    year={2017},
    eprint={1705.00652},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}
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