veriscrape-sbert-auto-wo-ref-deepseek-chat-0324

SentenceTransformer based on Alibaba-NLP/gte-base-en-v1.5

This is a sentence-transformers model finetuned from Alibaba-NLP/gte-base-en-v1.5. It maps sentences & paragraphs to a 768-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: Alibaba-NLP/gte-base-en-v1.5 <!-- at revision a829fd0e060bb84554da0dfd354d0de0f7712b7f -->
• Maximum Sequence Length: 64 tokens
• Output Dimensionality: 768 dimensions
• Similarity Function: Cosine Similarity

<!-- - **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({'max_seq_length': 64, 'do_lower_case': False}) with Transformer model: NewModel 
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, '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("albertus-sussex/veriscrape-sbert-auto-wo-ref-deepseek-chat-0324")
# Run inference
sentences = [
    '$27,174',
    'The data provided by Autodata is provided AS IS without warranty or guarantee of any kind, and Autodata disclaims all warranties or conditions of any kind, expressed or implied, with respect to such data, including the implied warranties of merchantable quality and fitness for a particular purpose.',
    '$39,890',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

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

<!-- ### 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.* -->

Evaluation

Metrics

Triplet

• Evaluated with <code>TripletEvaluator</code>

Silhouette

• Evaluated with <code>veriscrape.training.SilhouetteEvaluator</code>

Triplet

• Evaluated with <code>TripletEvaluator</code>

Silhouette

• Evaluated with <code>veriscrape.training.SilhouetteEvaluator</code>

<!-- ## 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: 35,294 training samples
• Columns: <code>anchor</code>, <code>positive</code>, <code>negative</code>, <code>pos_attr_name</code>, <code>neg_attr_name</code>, and <code>website_id</code>
• Approximate statistics based on the first 1000 samples:

  	anchor	positive	negative	pos_attr_name	neg_attr_name	website_id
  type	string	string	string	string	string	int
  details	<ul><li>min: 3 tokens</li><li>mean: 8.16 tokens</li><li>max: 64 tokens</li></ul>	<ul><li>min: 3 tokens</li><li>mean: 8.98 tokens</li><li>max: 64 tokens</li></ul>	<ul><li>min: 3 tokens</li><li>mean: 9.5 tokens</li><li>max: 64 tokens</li></ul>	<ul><li>min: 3 tokens</li><li>mean: 3.27 tokens</li><li>max: 5 tokens</li></ul>	<ul><li>min: 3 tokens</li><li>mean: 3.59 tokens</li><li>max: 5 tokens</li></ul>	<ul><li>0: ~4.00%</li><li>1: ~2.10%</li><li>2: ~3.50%</li><li>3: ~3.60%</li><li>4: ~4.70%</li><li>5: ~63.80%</li><li>6: ~3.20%</li><li>7: ~3.50%</li><li>8: ~7.50%</li><li>9: ~4.10%</li></ul>

• Samples:

  anchor	positive	negative	pos_attr_name	neg_attr_name	website_id
  <code>$34,270</code>	<code>$22,240</code>	<code>Lexus GX 460 Base 4dr AWD</code>	<code>price</code>	<code>model</code>	<code>0</code>
  <code>FWD or AWD</code>	<code>-</code>	<code>GT-R</code>	<code>engine</code>	<code>model</code>	<code>5</code>
  <code>-</code>	<code>$15,195</code>	<code>City: <br> <br> 11 <br> <br> <br> <br> <br>   Highway: <br> <br> 17<br> – <br> <br> 18</code>	<code>engine</code>	<code>fuel_economy</code>	<code>5</code>

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

  {
      "distance_metric": "TripletDistanceMetric.EUCLIDEAN",
      "triplet_margin": 5
  }
  

Evaluation Dataset

Unnamed Dataset

• Size: 3,922 evaluation samples
• Columns: <code>anchor</code>, <code>positive</code>, <code>negative</code>, <code>pos_attr_name</code>, <code>neg_attr_name</code>, and <code>website_id</code>
• Approximate statistics based on the first 1000 samples:

  	anchor	positive	negative	pos_attr_name	neg_attr_name	website_id
  type	string	string	string	string	string	int
  details	<ul><li>min: 3 tokens</li><li>mean: 8.4 tokens</li><li>max: 64 tokens</li></ul>	<ul><li>min: 3 tokens</li><li>mean: 8.63 tokens</li><li>max: 64 tokens</li></ul>	<ul><li>min: 3 tokens</li><li>mean: 9.54 tokens</li><li>max: 64 tokens</li></ul>	<ul><li>min: 3 tokens</li><li>mean: 3.21 tokens</li><li>max: 5 tokens</li></ul>	<ul><li>min: 3 tokens</li><li>mean: 3.63 tokens</li><li>max: 5 tokens</li></ul>	<ul><li>0: ~3.70%</li><li>1: ~2.50%</li><li>2: ~3.80%</li><li>3: ~3.30%</li><li>4: ~5.00%</li><li>5: ~62.20%</li><li>6: ~4.40%</li><li>7: ~3.90%</li><li>8: ~7.70%</li><li>9: ~3.50%</li></ul>

• Samples:

  anchor	positive	negative	pos_attr_name	neg_attr_name	website_id
  <code>$23,215</code>	<code>$95,465</code>	<code>$245,000</code>	<code>engine</code>	<code>price</code>	<code>5</code>
  <code>Visit our partners:</code>	<code>	</code>	<code>$32,000 – $38,000</code>	<code>engine</code>	<code>price</code>
  <code>$44,605</code>	<code>$22,530</code>	<code>22 mpg city / 33 mpg hwy</code>	<code>price</code>	<code>fuel_economy</code>	<code>9</code>

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

  {
      "distance_metric": "TripletDistanceMetric.EUCLIDEAN",
      "triplet_margin": 5
  }
  

Training Hyperparameters

Non-Default Hyperparameters

• eval_strategy: epoch
• per_device_train_batch_size: 128
• per_device_eval_batch_size: 128
• num_train_epochs: 5
• warmup_ratio: 0.1

All Hyperparameters

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

• overwrite_output_dir: False
• do_predict: False
• eval_strategy: epoch
• prediction_loss_only: True
• per_device_train_batch_size: 128
• per_device_eval_batch_size: 128
• 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: 1.0
• num_train_epochs: 5
• max_steps: -1
• lr_scheduler_type: linear
• lr_scheduler_kwargs: {}
• warmup_ratio: 0.1
• 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: False
• 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}
• 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: False
• hub_always_push: False
• gradient_checkpointing: False
• gradient_checkpointing_kwargs: None
• include_inputs_for_metrics: False
• 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
• dispatch_batches: None
• split_batches: 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
• prompts: None
• batch_sampler: batch_sampler
• multi_dataset_batch_sampler: proportional

</details>

Training Logs

Framework Versions

• Python: 3.10.16
• Sentence Transformers: 4.1.0
• Transformers: 4.45.2
• PyTorch: 2.5.1+cu124
• Accelerate: 1.6.0
• Datasets: 3.1.0
• Tokenizers: 0.20.3

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",
}

TripletLoss

@misc{hermans2017defense,
    title={In Defense of the Triplet Loss for Person Re-Identification},
    author={Alexander Hermans and Lucas Beyer and Bastian Leibe},
    year={2017},
    eprint={1703.07737},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}

<!-- ## Glossary *Clearly define terms in order to be accessible across audiences.* --> <!-- ## Model Card Authors *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.* --> <!-- ## Model Card Contact *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.* -->