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
Maximum Sequence Length: 384 tokens
Output Dimensionality: 768 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': 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.

python

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 &lt;xAOD::TFileMerger::addFile&gt;: /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]])

Evaluation

Metrics

Information Retrieval

Dataset: validation
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.745
cosine_accuracy@3	0.8583
cosine_accuracy@5	0.8867
cosine_accuracy@10	0.9183
cosine_precision@1	0.745
cosine_precision@3	0.2861
cosine_precision@5	0.1773
cosine_precision@10	0.0918
cosine_recall@1	0.745
cosine_recall@3	0.8583
cosine_recall@5	0.8867
cosine_recall@10	0.9183
cosine_ndcg@10	0.8348
cosine_mrr@10	0.8078
cosine_map@100	0.8109
dot_accuracy@1	0.745
dot_accuracy@3	0.8583
dot_accuracy@5	0.8867
dot_accuracy@10	0.9183
dot_precision@1	0.745
dot_precision@3	0.2861
dot_precision@5	0.1773
dot_precision@10	0.0918
dot_recall@1	0.745
dot_recall@3	0.8583
dot_recall@5	0.8867
dot_recall@10	0.9183
dot_ndcg@10	0.8348
dot_mrr@10	0.8078
dot_map@100	0.8109

Training Details

Training Dataset

Unnamed Dataset

Size: 12,000 training samples
Columns: anchor and positive
Approximate statistics based on the first 1000 samples:
anchor positive
type string string
details
min: 11 tokens
mean: 29.43 tokens
max: 93 tokens
min: 33 tokens
mean: 142.95 tokens
max: 356 tokens

Samples:

anchor	positive
`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?`	Metadata:[object Object]source: twiki[object Object]name: [object Object]version: 51[object Object]last modification: 09-09-2024[object Object]category: trigger[object Object]parents_structure: 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[object Object][object Object]Chunk text:[object Object]* 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).
`How can I list all available nox sessions using the uv runner?`	Metadata:[object Object]source: GitLabMarkdown[object Object]project path: particlepredatorinvasion/digout[object Object]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.[object Object]file path: docs/source/development/tests.md[object Object]header path: 'Testing & Automation' > 'Running Sessions'[object Object][object Object]Chunk text:[object Object][object Object][object Object][object Object] [object Object][object Object][object Object][object Object][object Object]For example, to run the linter: [object Object].
`Which setupATLAS -c options will set up the default CentOS6 container used by ATLAS?`	`Metadata:[object Object]source: AtlasTalk[object Object][object Object]Chunk text:[object Object]Answer 5:[object Object]Hi,[object Object]You can also do[object Object]setupATLAS -c centos6[object Object]setupATLAS -c sl6[object Object]setupATLAS -c rhel6[object Object]and it will always setup the default centos6 container that is used by ATLAS.`

Loss: MultipleNegativesRankingLoss with these parameters:
json
1{ 2 "scale": 1.0, 3 "similarity_fct": "dot_score", 4 "gather_across_devices": false 5}

Evaluation Dataset

Unnamed Dataset

Size: 1,200 evaluation samples
Columns: anchor and positive
Approximate statistics based on the first 1000 samples:
anchor positive
type string string
details
min: 8 tokens
mean: 28.49 tokens
max: 100 tokens
min: 38 tokens
mean: 142.38 tokens
max: 384 tokens

Samples:

anchor	positive
`Which copytool was used when the file transfer failed according to the error message?`	`Metadata:[object Object]source: AtlasTalk[object Object][object Object]Chunk text:[object Object]No matching replicas were found in list_replicas() output: [ReplicasNotFound(('No replica found for lfn=panda.0911140145.367865.lib._30337145.30050914397.lib.tgz (allow_lan=True, allow_wan=False)',), {})]:failed to transfer files using copytools=['rucio']`
`What are the dimensions of the single conductor wire used in SMC_set10 model set #10?`	`Metadata:[object Object]source: GitLabMarkdown[object Object]project path: steam/analyses/esc-on-smc[object Object]project description: [object Object]file path: SMC_set10/README.md[object Object]header path: 'Model set #10'[object Object][object Object]Chunk text:[object Object]Its conductor is a single 2 mm * 0.5 mm wire, but in ROXIE it has 4x4 current lines.`
`Where should an author go to submit an ATLAS internal note to the CERN Document Server (CDS)?`	`Metadata:[object Object]source: twiki[object Object]name: [object Object]version: 5[object Object]last modification: 19-04-2022[object Object]category: pubcom[object Object]parents_structure: P, u, b, C, o, m[object Object][object Object]Chunk text:[object Object]For each ATLAS internal note the following should be done:[object Object] * go to the [[[object Object] submission page for ATLAS notes]]: =[object Object]`

Loss: MultipleNegativesRankingLoss with these parameters:
json
1{ 2 "scale": 1.0, 3 "similarity_fct": "dot_score", 4 "gather_across_devices": false 5}

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

Click to expand

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: {'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: 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: {}

Training Logs

Epoch	Step	Training Loss	Validation Loss	validation_cosine_ndcg@10
0.5333	100	2.3423	2.2474	0.7773
1.064	200	2.2441	2.1880	0.8141
1.5973	300	2.208	2.1673	0.8285
2.128	400	2.1906	2.1575	0.8343
2.6613	500	2.1826	2.1530	0.8348

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

bibtex

@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

bibtex

@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}
}

	anchor	positive
type	string	string
details	min: 11 tokens mean: 29.43 tokens max: 93 tokens	min: 33 tokens mean: 142.95 tokens max: 356 tokens