Instructions to use csolaina/lora_r4_a16_d5_bs16_ep1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use csolaina/lora_r4_a16_d5_bs16_ep1 with sentence-transformers:

from sentence_transformers import SentenceTransformer

model = SentenceTransformer("csolaina/lora_r4_a16_d5_bs16_ep1")

sentences = [
    "predict",
    "    def _compute_score_samples(self, X, subsample_features):\n        \"\"\"\n        Compute the score of each samples in X going through the extra trees.\n\n        Parameters\n        ----------\n        X : array-like or sparse matrix\n            Data matrix.\n\n        subsample_features : bool\n            Whether features should be subsampled.\n\n        Returns\n        -------\n        scores : ndarray of shape (n_samples,)\n            The score of each sample in X.\n        \"\"\"\n        n_samples = X.shape[0]\n\n        depths = np.zeros(n_samples, order=\"f\")\n\n        average_path_length_max_samples = _average_path_length([self._max_samples])\n\n        # Note: we use default n_jobs value, i.e. sequential computation, which\n        # we expect to be more performant that parallelizing for small number\n        # of samples, e.g. < 1k samples. Default n_jobs value can be overridden\n        # by using joblib.parallel_backend context manager around\n        # ._compute_score_samples. Using a higher n_jobs may speed up the\n        # computation of the scores, e.g. for > 1k samples. See\n        # https://github.com/scikit-learn/scikit-learn/pull/28622 for more\n        # details.\n        lock = threading.Lock()\n        Parallel(\n            verbose=self.verbose,\n            require=\"sharedmem\",\n        )(\n            delayed(_parallel_compute_tree_depths)(\n                tree,\n                X,\n                features if subsample_features else None,\n                self._decision_path_lengths[tree_idx],\n                self._average_path_length_per_tree[tree_idx],\n                depths,\n                lock,\n            )\n            for tree_idx, (tree, features) in enumerate(\n                zip(self.estimators_, self.estimators_features_)\n            )\n        )\n\n        denominator = len(self.estimators_) * average_path_length_max_samples\n        scores = 2 ** (\n            # For a single training sample, denominator and depth are 0.\n            # Therefore, we set the score manually to 1.\n            -np.divide(\n                depths, denominator, out=np.ones_like(depths), where=denominator != 0\n            )\n        )\n        return scores",
    "        def predict(self, X):\n            return np.zeros(X.shape[0])",
    "def test_dist_threshold_invalid_parameters():\n    X = [[0], [1]]\n    with pytest.raises(ValueError, match=\"Exactly one of \"):\n        AgglomerativeClustering(n_clusters=None, distance_threshold=None).fit(X)\n\n    with pytest.raises(ValueError, match=\"Exactly one of \"):\n        AgglomerativeClustering(n_clusters=2, distance_threshold=1).fit(X)\n\n    X = [[0], [1]]\n    with pytest.raises(ValueError, match=\"compute_full_tree must be True if\"):\n        AgglomerativeClustering(\n            n_clusters=None, distance_threshold=1, compute_full_tree=False\n        ).fit(X)"
]
embeddings = model.encode(sentences)

similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [4, 4]

Notebooks
Google Colab
Kaggle

How to use from the

Use from the

sentence-transformers library

from sentence_transformers import SentenceTransformer

model = SentenceTransformer("csolaina/lora_r4_a16_d5_bs16_ep1")

sentences = [
    "predict",
    "    def _compute_score_samples(self, X, subsample_features):\n        \"\"\"\n        Compute the score of each samples in X going through the extra trees.\n\n        Parameters\n        ----------\n        X : array-like or sparse matrix\n            Data matrix.\n\n        subsample_features : bool\n            Whether features should be subsampled.\n\n        Returns\n        -------\n        scores : ndarray of shape (n_samples,)\n            The score of each sample in X.\n        \"\"\"\n        n_samples = X.shape[0]\n\n        depths = np.zeros(n_samples, order=\"f\")\n\n        average_path_length_max_samples = _average_path_length([self._max_samples])\n\n        # Note: we use default n_jobs value, i.e. sequential computation, which\n        # we expect to be more performant that parallelizing for small number\n        # of samples, e.g. < 1k samples. Default n_jobs value can be overridden\n        # by using joblib.parallel_backend context manager around\n        # ._compute_score_samples. Using a higher n_jobs may speed up the\n        # computation of the scores, e.g. for > 1k samples. See\n        # https://github.com/scikit-learn/scikit-learn/pull/28622 for more\n        # details.\n        lock = threading.Lock()\n        Parallel(\n            verbose=self.verbose,\n            require=\"sharedmem\",\n        )(\n            delayed(_parallel_compute_tree_depths)(\n                tree,\n                X,\n                features if subsample_features else None,\n                self._decision_path_lengths[tree_idx],\n                self._average_path_length_per_tree[tree_idx],\n                depths,\n                lock,\n            )\n            for tree_idx, (tree, features) in enumerate(\n                zip(self.estimators_, self.estimators_features_)\n            )\n        )\n\n        denominator = len(self.estimators_) * average_path_length_max_samples\n        scores = 2 ** (\n            # For a single training sample, denominator and depth are 0.\n            # Therefore, we set the score manually to 1.\n            -np.divide(\n                depths, denominator, out=np.ones_like(depths), where=denominator != 0\n            )\n        )\n        return scores",
    "        def predict(self, X):\n            return np.zeros(X.shape[0])",
    "def test_dist_threshold_invalid_parameters():\n    X = [[0], [1]]\n    with pytest.raises(ValueError, match=\"Exactly one of \"):\n        AgglomerativeClustering(n_clusters=None, distance_threshold=None).fit(X)\n\n    with pytest.raises(ValueError, match=\"Exactly one of \"):\n        AgglomerativeClustering(n_clusters=2, distance_threshold=1).fit(X)\n\n    X = [[0], [1]]\n    with pytest.raises(ValueError, match=\"compute_full_tree must be True if\"):\n        AgglomerativeClustering(\n            n_clusters=None, distance_threshold=1, compute_full_tree=False\n        ).fit(X)"
]
embeddings = model.encode(sentences)

similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [4, 4]

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

	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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Base model

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Papers for csolaina/lora_r4_a16_d5_bs16_ep1

Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks

Paper • 1908.10084 • Published Aug 27, 2019 • 19

Efficient Natural Language Response Suggestion for Smart Reply

Paper • 1705.00652 • Published May 1, 2017