jeffreylimnardy/sensor-e5-finetuned

SentenceTransformer based on intfloat/multilingual-e5-small

This is a sentence-transformers model finetuned from intfloat/multilingual-e5-small. 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/multilingual-e5-small <!-- at revision c007d7ef6fd86656326059b28395a7a03a7c5846 -->
• —Maximum Sequence Length: 512 tokens
• —Output Dimensionality: 384 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': 512, 'do_lower_case': False, 'architecture': 'BertModel'})
  (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("jeffreylimnardy/sensor-e5-finetuned")
# Run inference
sentences = [
    "passage: name: GWM Spechtstraße, description: Grundwasserpegelmessung (Standort: Spechtstraße), datastream: [{'name': 'Wasserpegel an der GWM Spechtstraße', 'description': 'Der Grundwasserpegel bezogen auf NN: Abstand zwischen Meereshöhe und Wasseroberfläche'}, {'name': '(Wasser-)Temperatur an Sonde der GWM Spechtstraße', 'description': 'Die Temperatur an der Sonde (im Wasser)'}, {'name': 'Atmosphärischer Druck an der GWM Spechtstraße', 'description': 'Der Atmosphärische Druck an der Sendeeinheit (über dem Wasser)'}, {'name': 'Umgebungstemperatur an der GWM Spechtstraße', 'description': 'Die Umgebungstemperatur, gemessen an der Sendeeinheit (über dem Wasser)'}, {'name': 'Differenzdruck an der GWM Spechtstraße', 'description': 'Die Differenz zwischen dem Druck an der Sonde (im Wasser) und dem atmosphärischen Druck an der Sendeeinheit'}]",
    "passage: name: GWM Wittenfelde Mühlendamm, description: Grundwasserpegelmessung (Standort: Wittenfelde Mühlendamm, datastream: [{'name': 'Wasserpegel an der GWM Wittenfelde Mühlendamm', 'description': 'Der Grundwasserpegel bezogen auf NN: Abstand zwischen Meereshöhe und Wasseroberfläche'}, {'name': '(Wasser-)Temperatur an Sonde der GWM Wittenfelde Mühlendamm', 'description': 'Die Temperatur an der Sonde (im Wasser)'}, {'name': 'Atmosphärischer Druck an der GWM Wittenfelde Mühlendamm', 'description': 'Der Atmosphärische Druck an der Sendeeinheit (über dem Wasser)'}, {'name': 'Umgebungstemperatur an der GWM Wittenfelde Mühlendamm', 'description': 'Die Umgebungstemperatur, gemessen an der Sendeeinheit (über dem Wasser)'}, {'name': 'Differenzdruck an der GWM Wittenfelde Mühlendamm', 'description': 'Die Differenz zwischen dem Druck an der Sonde (im Wasser) und dem atmosphärischen Druck an der Sendeeinheit'}]",
    "passage: name: Parkplatz für Menschen mit Behinderung Alte Münze - 02, description: Parkplatz (Standort: Alte Münze - 02); vor Hausnummer 10, datastream: [{'name': 'Belegtstatus an dem Parkplatz Alte Münze - 02', 'description': 'Belegtstatus'}, {'name': 'Temperatur an dem Parkplatz Alte Münze - 02', 'description': 'Temperatur an dem Parksensor'}]",
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.9743, 0.3447],
#         [0.9743, 1.0000, 0.3384],
#         [0.3447, 0.3384, 1.0000]])

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Evaluation

Metrics

Triplet

• —Dataset: sensor-eval
• —Evaluated with <code>TripletEvaluator</code>

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Training Details

Training Dataset

Unnamed Dataset

• —Size: 255 training samples
• —Columns: <code>anchor</code> and <code>positive</code>
• —Approximate statistics based on the first 255 samples: | | anchor | positive | |:--------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------| | type | string | string | | details | <ul><li>min: 84 tokens</li><li>mean: 186.89 tokens</li><li>max: 319 tokens</li></ul> | <ul><li>min: 84 tokens</li><li>mean: 186.13 tokens</li><li>max: 319 tokens</li></ul> |
• —Samples: | anchor | positive | |:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>passage: name: GWM Wittenfelde, description: Grundwasserpegelmessung (Standort: Wittenfelde), datastream: [{'name': 'Wasserpegel an der GWM Wittenfelde', 'description': 'Der Grundwasserpegel bezogen auf NN: Abstand zwischen Meereshöhe und Wasseroberfläche'}, {'name': '(Wasser-)Temperatur an Sonde der GWM Wittenfelde', 'description': 'Die Temperatur an der Sonde (im Wasser)'}, {'name': 'Atmosphärischer Druck an der GWM Wittenfelde', 'description': 'Der Atmosphärische Druck an der Sendeeinheit (über dem Wasser)'}, {'name': 'Umgebungstemperatur an der GWM Wittenfelde', 'description': 'Die Umgebungstemperatur, gemessen an der Sendeeinheit (über dem Wasser)'}, {'name': 'Differenzdruck an der GWM Wittenfelde', 'description': 'Die Differenz zwischen dem Druck an der Sonde (im Wasser) und dem atmosphärischen Druck an der Sendeeinheit'}]</code> | <code>passage: name: GWM Spechtstraße, description: Grundwasserpegelmessung (Standort: Spechtstraße), datastream: [{'name': 'Wasserpegel an der GWM Spechtstraße', 'description': 'Der Grundwasserpegel bezogen auf NN: Abstand zwischen Meereshöhe und Wasseroberfläche'}, {'name': '(Wasser-)Temperatur an Sonde der GWM Spechtstraße', 'description': 'Die Temperatur an der Sonde (im Wasser)'}, {'name': 'Atmosphärischer Druck an der GWM Spechtstraße', 'description': 'Der Atmosphärische Druck an der Sendeeinheit (über dem Wasser)'}, {'name': 'Umgebungstemperatur an der GWM Spechtstraße', 'description': 'Die Umgebungstemperatur, gemessen an der Sendeeinheit (über dem Wasser)'}, {'name': 'Differenzdruck an der GWM Spechtstraße', 'description': 'Die Differenz zwischen dem Druck an der Sonde (im Wasser) und dem atmosphärischen Druck an der Sendeeinheit'}]</code> | | <code>passage: name: Parkplatz für Menschen mit Behinderung Möserstrasse Hs 4 - 02, description: Parkplatz (Standort: Möserstrasse Hs 4 - 02), datastream: [{'name': 'Belegtstatus an dem Parkplatz Möserstrasse Hs 4 - 02', 'description': 'Belegtstatus'}, {'name': 'Temperatur an dem Parkplatz Möserstrasse Hs 4 - 02', 'description': 'Temperatur an dem Parksensor'}]</code> | <code>passage: name: Parkplatz für Menschen mit Behinderung Schillerstrasse - 03, description: Parkplatz (Standort: Schillerstrasse - 03), datastream: [{'name': 'Belegtstatus an dem Parkplatz Schillerstrasse - 03', 'description': 'Belegtstatus'}, {'name': 'Temperatur an dem Parkplatz Schillerstrasse - 03', 'description': 'Temperatur an dem Parksensor'}]</code> | | <code>passage: name: GWM Spechtstraße, description: Grundwasserpegelmessung (Standort: Spechtstraße), datastream: [{'name': 'Wasserpegel an der GWM Spechtstraße', 'description': 'Der Grundwasserpegel bezogen auf NN: Abstand zwischen Meereshöhe und Wasseroberfläche'}, {'name': '(Wasser-)Temperatur an Sonde der GWM Spechtstraße', 'description': 'Die Temperatur an der Sonde (im Wasser)'}, {'name': 'Atmosphärischer Druck an der GWM Spechtstraße', 'description': 'Der Atmosphärische Druck an der Sendeeinheit (über dem Wasser)'}, {'name': 'Umgebungstemperatur an der GWM Spechtstraße', 'description': 'Die Umgebungstemperatur, gemessen an der Sendeeinheit (über dem Wasser)'}, {'name': 'Differenzdruck an der GWM Spechtstraße', 'description': 'Die Differenz zwischen dem Druck an der Sonde (im Wasser) und dem atmosphärischen Druck an der Sendeeinheit'}]</code> | <code>passage: name: GWM Wittenfelde Mühlendamm, description: Grundwasserpegelmessung (Standort: Wittenfelde Mühlendamm, datastream: [{'name': 'Wasserpegel an der GWM Wittenfelde Mühlendamm', 'description': 'Der Grundwasserpegel bezogen auf NN: Abstand zwischen Meereshöhe und Wasseroberfläche'}, {'name': '(Wasser-)Temperatur an Sonde der GWM Wittenfelde Mühlendamm', 'description': 'Die Temperatur an der Sonde (im Wasser)'}, {'name': 'Atmosphärischer Druck an der GWM Wittenfelde Mühlendamm', 'description': 'Der Atmosphärische Druck an der Sendeeinheit (über dem Wasser)'}, {'name': 'Umgebungstemperatur an der GWM Wittenfelde Mühlendamm', 'description': 'Die Umgebungstemperatur, gemessen an der Sendeeinheit (über dem Wasser)'}, {'name': 'Differenzdruck an der GWM Wittenfelde Mühlendamm', 'description': 'Die Differenz zwischen dem Druck an der Sonde (im Wasser) und dem atmosphärischen Druck an der Sendeeinheit'}]</code> |
• —Loss: <code>MultipleNegativesRankingLoss</code> with these parameters:

  {
      "scale": 20.0,
      "similarity_fct": "cos_sim",
      "gather_across_devices": false
  }

Training Hyperparameters

Non-Default Hyperparameters

• —per_device_train_batch_size: 4
• —num_train_epochs: 40
• —warmup_steps: 10
• —gradient_accumulation_steps: 4
• —eval_strategy: epoch
• —load_best_model_at_end: True

All Hyperparameters

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

• —per_device_train_batch_size: 4
• —num_train_epochs: 40
• —max_steps: -1
• —learning_rate: 5e-05
• —lr_scheduler_type: linear
• —lr_scheduler_kwargs: None
• —warmup_steps: 10
• —optim: adamwtorchfused
• —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: 4
• —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: trackio
• —eval_strategy: epoch
• —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: True
• —ignore_data_skip: False
• —restore_callback_states_from_checkpoint: False
• —full_determinism: False
• —seed: 42
• —data_seed: None
• —use_cpu: False
• —accelerator_config: {'splitbatches': False, 'dispatchbatches': None, 'evenbatches': True, 'useseedablesampler': True, 'nonblocking': False, 'gradientaccumulationkwargs': None}
• —parallelism_config: None
• —dataloader_drop_last: False
• —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_backend: None
• —ddp_timeout: 1800
• —fsdp: []
• —fsdp_config: {'minnumparams': 0, 'xla': False, 'xlafsdpv2': False, 'xlafsdpgrad_ckpt': False}
• —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

Framework Versions

• —Python: 3.12.12
• —Sentence Transformers: 5.2.3
• —Transformers: 5.2.0
• —PyTorch: 2.10.0
• —Accelerate: 1.12.0
• —Datasets: 4.6.0
• —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{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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