csne-minilm-retrieval-full-1ep

SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2

This is a sentence-transformers model finetuned from sentence-transformers/all-MiniLM-L6-v2. It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for retrieval.

Model Details

Model Description

• Model Type: Sentence Transformer
• Base model: sentence-transformers/all-MiniLM-L6-v2 <!-- at revision 1110a243fdf4706b3f48f1d95db1a4f5529b4d41 -->
• Maximum Sequence Length: 256 tokens
• Output Dimensionality: 384 dimensions
• Similarity Function: Cosine Similarity
• Supported Modality: Text

<!-- - **Training Dataset:** Unknown --> <!-- - **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': 'BertModel'})
  (1): Pooling({'embedding_dimension': 384, 'pooling_mode': 'mean', '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("opnsrcntrbtrian/csne-minilm-retrieval-full-1ep")
# Run inference
queries = [
    'A shortcut to render a ``script`` tag for the packaged jQuery',
]
documents = [
    "jquery_js def jquery_js(version=None, migrate=False): pass version = version or settings.JQUERY_VERSION suffix = '.min' if not settings.DEBUG else '' libs = [js_lib('jquery-%s%s.js' % (version, suffix))] if _boolean(migrate): libs.append(js_lib('jquery-migrate-%s%s.js' % (JQUERY_MIGRATE_VERSION, suffix))) return '\\n'.join(libs)",
    "api_call def api_call(method, end_point, params=None, client_id=None, access_token=None): pass if bool(client_id) == bool(access_token): raise ValueError('Either client_id or access_token') url = 'https://api.quizlet.com/2.0/{}'.format(end_point) if not params: params = {} if client_id: params['client_id'] = client_id headers = {'Authorization': 'Bearer {}'.format(access_token)} if access_token else None response = requests.request(method, url, params=params, headers=headers) if int(response.status_code / 100) != 2: error_title = '' try: error_title += ', ' + response.json()['error_title'] except ValueError: pass except KeyError: pass raise ValueError('{} returned {}{}'.format(url, response.status_code, error_title)) try: return response.json() except json.decoder.JSONDecodeError: pass",
    '_transaction_func def _transaction_func(entering, exiting, using): pass if callable(using): return Transaction(entering, exiting, None)(using) return Transaction(entering, exiting, using)',
]
query_embeddings = model.encode_query(queries)
document_embeddings = model.encode_document(documents)
print(query_embeddings.shape, document_embeddings.shape)
# [1, 384] [3, 384]

# Get the similarity scores for the embeddings
similarities = model.similarity(query_embeddings, document_embeddings)
print(similarities)
# tensor([[ 0.5674, -0.0050, -0.0192]])

<!-- ### Direct Usage (Transformers) <details><summary>Click to see the direct usage in Transformers</summary> </details> --> <!-- ### Downstream Usage (Sentence Transformers) You can finetune this model on your own dataset. <details><summary>Click to expand</summary> </details> --> <!-- ### Out-of-Scope Use *List how the model may foreseeably be misused and address what users ought not to do with the model.* --> <!-- ## Bias, Risks and Limitations *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* --> <!-- ### Recommendations *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* -->

Training Details

Training Dataset

Unnamed Dataset

• Size: 393,427 training samples
• Columns: <code>query</code> and <code>code</code>
• Approximate statistics based on the first 100 samples:

  	query	code
  type	string	string
  modality	text	text
  details	<ul><li>min: 5 tokens</li><li>mean: 42.38 tokens</li><li>max: 256 tokens</li></ul>	<ul><li>min: 39 tokens</li><li>mean: 142.72 tokens</li><li>max: 256 tokens</li></ul>

• Samples:

  query	code
  <code>Estimate discontinuity in basis of low resolution image segmentation.</code>	<code>ImageGraphCut.__msgc_step3_discontinuity_localization def __msgc_step3_discontinuity_localization(self): pass import scipy start = self._start_time seg = 1 - self.segmentation.astype(np.int8) self.stats['low level object voxels'] = np.sum(seg) self.stats['low level image voxels'] = np.prod(seg.shape) seg_border = scipy.ndimage.filters.laplace(seg, mode='constant') logger.debug('seg_border: %s', scipy.stats.describe(seg_border, axis=None)) seg_border[seg_border != 0] = 1 logger.debug('seg_border: %s', scipy.stats.describe(seg_border, axis=None)) boundary_dilatation_distance = self.segparams['boundary_dilatation_distance'] seg = scipy.ndimage.morphology.binary_dilation(seg_border, np.ones([boundary_dilatation_distance * 2 + 1, boundary_dilatation_distance * 2 + 1, boundary_dilatation_distance * 2 + 1])) if self.keep_temp_properties: self.temp_msgc_lowres_discontinuity = seg else: self.temp_msgc_lowres_discontinuity = None if self.debug_images: import sed3 pd = sed3.sed3(seg_border) pd.sh...</code>
  <code>Run Graph-Cut segmentation with refinement of low resolution multiscale graph. In first step is performed normal GC on low resolution data Second step construct finer grid on edges of segmentation from first step. There is no option for use without use_boundary_penalties</code>	<code>ImageGraphCut.__multiscale_gc_lo2hi_run def __multiscale_gc_lo2hi_run(self): pass self._msgc_lo2hi_resize_init() self.__msgc_step0_init() hard_constraints = self.__msgc_step12_low_resolution_segmentation() seg = self.__msgc_step3_discontinuity_localization() self.stats['t3.1'] = time.time() - self._start_time graph = Graph(seg, voxelsize=self.voxelsize, nsplit=self.segparams['block_size'], edge_weight_table=self._msgc_npenalty_table, compute_low_nodes_index=True) graph.generate_base_grid() self.stats['t3.2'] = time.time() - self._start_time graph.split_voxels() self.stats['t3.3'] = time.time() - self._start_time self.stats.update(graph.stats) self.stats['t4'] = time.time() - self._start_time mul_mask, mul_val = self.__msgc_tlinks_area_weight_from_low_segmentation(seg) area_weight = 1 unariesalt = self.__create_tlinks(self.img, self.voxelsize, self.seeds, area_weight=area_weight, hard_constraints=hard_constraints, mul_mask=None, mul_val=None) self.stats['t5'] = time.time() - self._start...</code>
  <code>Run Graph-Cut segmentation with simplifiyng of high resolution multiscale graph. In first step is performed normal GC on low resolution data Second step construct finer grid on edges of segmentation from first step. There is no option for use without use_boundary_penalties</code>	<code>ImageGraphCut.__multiscale_gc_hi2lo_run def __multiscale_gc_hi2lo_run(self): pass self.__msgc_step0_init() hard_constraints = self.__msgc_step12_low_resolution_segmentation() seg = self.__msgc_step3_discontinuity_localization() nlinks, unariesalt2, msinds = self.__msgc_step45678_hi2lo_construct_graph(hard_constraints, seg) self.__msgc_step9_finish_perform_gc_and_reshape(nlinks, unariesalt2, msinds)</code>

• Loss: <code>MultipleNegativesRankingLoss</code> with these parameters:

  {
      "scale": 20.0,
      "similarity_fct": "cos_sim",
      "gather_across_devices": false,
      "directions": [
          "query_to_doc"
      ],
      "partition_mode": "joint",
      "hardness_mode": null,
      "hardness_strength": 0.0
  }
  

Training Hyperparameters

Non-Default Hyperparameters

• per_device_train_batch_size: 64
• num_train_epochs: 1
• learning_rate: 2e-05
• warmup_steps: 0.1
• dataloader_drop_last: True

All Hyperparameters

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

• per_device_train_batch_size: 64
• num_train_epochs: 1
• max_steps: -1
• learning_rate: 2e-05
• lr_scheduler_type: linear
• lr_scheduler_kwargs: None
• warmup_steps: 0.1
• optim: adamw_torch_fused
• optim_args: None
• weight_decay: 0.0
• adam_beta1: 0.9
• adam_beta2: 0.999
• adam_epsilon: 1e-08
• optim_target_modules: None
• gradient_accumulation_steps: 1
• average_tokens_across_devices: True
• max_grad_norm: 1.0
• label_smoothing_factor: 0.0
• bf16: False
• fp16: False
• bf16_full_eval: False
• fp16_full_eval: False
• tf32: None
• gradient_checkpointing: False
• gradient_checkpointing_kwargs: None
• torch_compile: False
• torch_compile_backend: None
• torch_compile_mode: None
• use_liger_kernel: False
• liger_kernel_config: None
• use_cache: False
• neftune_noise_alpha: None
• torch_empty_cache_steps: None
• auto_find_batch_size: False
• log_on_each_node: True
• logging_nan_inf_filter: True
• include_num_input_tokens_seen: no
• log_level: passive
• log_level_replica: warning
• disable_tqdm: False
• project: huggingface
• trackio_space_id: None
• trackio_bucket_id: None
• trackio_static_space_id: None
• per_device_eval_batch_size: 8
• prediction_loss_only: True
• eval_on_start: False
• eval_do_concat_batches: True
• eval_use_gather_object: False
• eval_accumulation_steps: None
• include_for_metrics: []
• batch_eval_metrics: False
• save_only_model: False
• save_on_each_node: False
• enable_jit_checkpoint: False
• push_to_hub: False
• hub_private_repo: None
• hub_model_id: None
• hub_strategy: every_save
• hub_always_push: False
• hub_revision: None
• load_best_model_at_end: False
• ignore_data_skip: False
• restore_callback_states_from_checkpoint: False
• full_determinism: False
• seed: 42
• data_seed: None
• use_cpu: 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
• dataloader_drop_last: True
• dataloader_num_workers: 0
• dataloader_pin_memory: True
• dataloader_persistent_workers: False
• dataloader_prefetch_factor: None
• remove_unused_columns: True
• label_names: None
• train_sampling_strategy: random
• length_column_name: length
• ddp_find_unused_parameters: None
• ddp_bucket_cap_mb: None
• ddp_broadcast_buffers: False
• ddp_static_graph: None
• ddp_backend: None
• ddp_timeout: 1800
• fsdp: None
• fsdp_config: None
• deepspeed: None
• debug: []
• skip_memory_metrics: True
• do_predict: False
• resume_from_checkpoint: None
• warmup_ratio: None
• local_rank: -1
• prompts: None
• batch_sampler: batch_sampler
• multi_dataset_batch_sampler: proportional
• router_mapping: {}
• learning_rate_mapping: {}

</details>

Training Logs

Training Time

• Training: 1.5 hours

Framework Versions

• Python: 3.12.13
• Sentence Transformers: 5.6.0
• Transformers: 5.13.1
• PyTorch: 2.11.0+cu128
• Accelerate: 1.14.0
• Datasets: 4.0.0
• 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",
}

MultipleNegativesRankingLoss

@misc{oord2019representationlearningcontrastivepredictive,
      title={Representation Learning with Contrastive Predictive Coding},
      author={Aaron van den Oord and Yazhe Li and Oriol Vinyals},
      year={2019},
      eprint={1807.03748},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/1807.03748},
}

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