csolaina/lora_r4_a16_d5_bs16_ep1

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

• 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': 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}
}