kipark/all-mpnet-base-v2-combined_4400-400vs1000

SentenceTransformer based on sentence-transformers/all-mpnet-base-v2

This is a sentence-transformers model finetuned from sentence-transformers/all-mpnet-base-v2. 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: sentence-transformers/all-mpnet-base-v2 <!-- at revision e8c3b32edf5434bc2275fc9bab85f82640a19130 -->
• —Maximum Sequence Length: 384 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': 384, 'do_lower_case': False, 'architecture': 'MPNetModel'})
  (1): Pooling({'word_embedding_dimension': 768, '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("kipark/all-mpnet-base-v2-combined_4400-400vs1000")
# Run inference
sentences = [
    'Which file could not be opened according to the xAOD::TFileMerger::addFile error message?',
    'Metadata:\nsource: AtlasTalk\n\nChunk text:\nError in <xAOD::TFileMerger::addFile>: /build1/atnight/localbuilds/nightlies/AnalysisBase-2.3.X/AnalysisBase/rel_nightly/xAODRootAccess/Root/TFileMerger.cxx:105 Couldn\'t open file "user.pottgen.5855794._000003.hist-output.root"',
    "Metadata:\nsource: GitLabMarkdown\nproject path: acc-co/ucap/ucap-core\nproject description: \nfile path: docs/src/docs/reference/device-behavior.md\nheader path: 'Device Behavior' > 'Acquisition properties' > 'First updates'\n\nChunk text:\nAs of May 2024, UCAP retains converter outputs (for each selector) within an in-memory data structure, paired with the\nrelevant selector. Thus, UCAP nodes provide first-updates as needed for `get` and `subscribe` operations; however,",
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[ 1.0000,  0.7130, -0.0958],
#         [ 0.7130,  1.0000, -0.1120],
#         [-0.0958, -0.1120,  1.0000]])

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Downstream Usage (Sentence Transformers)

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Evaluation

Metrics

Information Retrieval

• —Dataset: validation
• —Evaluated with <code>InformationRetrievalEvaluator</code>

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Training Details

Training Dataset

Unnamed Dataset

• —Size: 12,000 training samples
• —Columns: <code>anchor</code> and <code>positive</code>
• —Approximate statistics based on the first 1000 samples: | | anchor | positive | |:--------|:-----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------| | type | string | string | | details | <ul><li>min: 11 tokens</li><li>mean: 29.43 tokens</li><li>max: 93 tokens</li></ul> | <ul><li>min: 33 tokens</li><li>mean: 142.95 tokens</li><li>max: 356 tokens</li></ul> |
• —Samples: | anchor | positive | |:-----------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>On the ATLAS Trigger Developer Pages, what do two-digit version numbers (e.g., 21.3) and three-digit version numbers (e.g., 21.3.9) indicate?</code> | <code>Metadata:<br>source: twiki<br>name: <br>version: 51<br>last modification: 09-09-2024<br>category: trigger<br>parentsstructure: W, e, b, H, o, m, e, /, A, t, l, a, s, T, r, i, g, g, e, r, /, T, r, i, g, g, e, r, D, e, v, e, l, o, p, e, r, P, a, g, e, s<br><br>Chunk text:<br>* Two-digit version numbers correspond the branch used to build the nightly (e.g. 21.3) while three digit version numbers correspond to built releases (21.3.9).</code> | | <code>How can I list all available nox sessions using the uv runner?</code> | <code>Metadata:<br>source: GitLabMarkdown<br>project path: particlepredatorinvasion/digout<br>project description: Configurable Python library that automates the conversion of LHCb DIGI files into parquet dataframes by managing a sequence of dependent steps and scheduling their parallel execution on local or distributed systems.<br>file path: docs/source/development/tests.md<br>header path: 'Testing & Automation' > 'Running Sessions'<br><br>Chunk text:<br>**To list all available sessions:**<br>```bash<br>uv run nox --list<br>``` <br>**To run a specific session:**<br>```bash<br>uv run nox -s <sessionname><br>``<br>For example, to run the linter: uv run nox -s lint_check`.</code> | | <code>Which setupATLAS -c options will set up the default CentOS6 container used by ATLAS?</code> | <code>Metadata:<br>source: AtlasTalk<br><br>Chunk text:<br>Answer 5:<br>Hi,<br>You can also do<br>setupATLAS -c centos6<br>setupATLAS -c sl6<br>setupATLAS -c rhel6<br>and it will always setup the default centos6 container that is used by ATLAS.</code> |
• —Loss: <code>MultipleNegativesRankingLoss</code> with these parameters:

  {
      "scale": 1.0,
      "similarity_fct": "dot_score",
      "gather_across_devices": false
  }

Evaluation Dataset

Unnamed Dataset

• —Size: 1,200 evaluation samples
• —Columns: <code>anchor</code> and <code>positive</code>
• —Approximate statistics based on the first 1000 samples: | | anchor | positive | |:--------|:-----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------| | type | string | string | | details | <ul><li>min: 8 tokens</li><li>mean: 28.49 tokens</li><li>max: 100 tokens</li></ul> | <ul><li>min: 38 tokens</li><li>mean: 142.38 tokens</li><li>max: 384 tokens</li></ul> |
• —Samples: | anchor | positive | |:-----------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>Which copytool was used when the file transfer failed according to the error message?</code> | <code>Metadata:<br>source: AtlasTalk<br><br>Chunk text:<br>No matching replicas were found in listreplicas() output: [ReplicasNotFound(('No replica found for lfn=panda.0911140145.367865.lib.30337145.30050914397.lib.tgz (allowlan=True, allowwan=False)',), {})]:failed to transfer files using copytools=['rucio']</code> | | <code>What are the dimensions of the single conductor wire used in SMCset10 model set #10?</code> | <code>Metadata:<br>source: GitLabMarkdown<br>project path: steam/analyses/esc-on-smc<br>project description: <br>file path: SMCset10/README.md<br>header path: 'Model set #10'<br><br>Chunk text:<br>Its conductor is a single 2 mm 0.5 mm wire, but in ROXIE it has 4x4 current lines.</code> | | <code>Where should an author go to submit an ATLAS internal note to the CERN Document Server (CDS)?</code> | <code>Metadata:<br>source: twiki<br>name: <br>version: 5<br>last modification: 19-04-2022<br>category: pubcom<br>parents_structure: P, u, b, C, o, m<br><br>Chunk text:<br>For each ATLAS internal note the following should be done:<br> go to the [[https://cds.cern.ch/submit?ln=en&doctype=ATN][CDS submission page for ATLAS notes]]: =https://cds.cern.ch/submit?ln=en&doctype=ATN=</code> |
• —Loss: <code>MultipleNegativesRankingLoss</code> with these parameters:

  {
      "scale": 1.0,
      "similarity_fct": "dot_score",
      "gather_across_devices": false
  }

Training Hyperparameters

Non-Default Hyperparameters

• —eval_strategy: steps
• —per_device_train_batch_size: 16
• —per_device_eval_batch_size: 16
• —gradient_accumulation_steps: 4
• —learning_rate: 5e-07
• —warmup_ratio: 0.1
• —fp16: True
• —batch_sampler: no_duplicates

All Hyperparameters

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

• —overwrite_output_dir: False
• —do_predict: False
• —eval_strategy: steps
• —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: 4
• —eval_accumulation_steps: None
• —torch_empty_cache_steps: None
• —learning_rate: 5e-07
• —weight_decay: 0.0
• —adam_beta1: 0.9
• —adam_beta2: 0.999
• —adam_epsilon: 1e-08
• —max_grad_norm: 1.0
• —num_train_epochs: 3
• —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: 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: {'minnumparams': 0, 'xla': False, 'xlafsdpv2': False, 'xlafsdpgrad_ckpt': False}
• —fsdp_transformer_layer_cls_to_wrap: None
• —accelerator_config: {'splitbatches': False, 'dispatchbatches': None, 'evenbatches': True, 'useseedablesampler': True, 'nonblocking': False, 'gradientaccumulationkwargs': 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
• —hub_revision: None
• —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
• —liger_kernel_config: None
• —eval_use_gather_object: False
• —average_tokens_across_devices: False
• —prompts: None
• —batch_sampler: no_duplicates
• —multi_dataset_batch_sampler: proportional
• —router_mapping: {}
• —learning_rate_mapping: {}

</details>

Training Logs

Framework Versions

• —Python: 3.12.11
• —Sentence Transformers: 5.1.0
• —Transformers: 4.55.2
• —PyTorch: 2.2.2+cu121
• —Accelerate: 1.10.1
• —Datasets: 4.0.0
• —Tokenizers: 0.21.4

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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