logembed_a2 / README.md

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metadata

tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:33174
  - loss:TripletLoss
base_model: sentence-transformers/multi-qa-mpnet-base-cos-v1
widget:
  - source_sentence: >-
      writeBlock blk_-2025444374149014902 received exception
      java.io.IOException: Could not read from stream
    sentences:
      - >-
        PAM 5 more authentication failures; logname= uid=0 euid=0 tty=ssh ruser=
        rhost=218.65.30.30 user=root
      - >-
        writeBlock blk_5718472814394212827 received exception
        java.io.IOException: Could not read from stream
      - Adding an already existing block blk_5697572983288390847
  - source_sentence: Accepted password for hxu from 137.189.206.152 port 13415 ssh2
    sentences:
      - >-
        Address 14.186.200.51 maps to static.vnpt.vn, but this does not map back
        to the address - POSSIBLE BREAK-IN ATTEMPT!
      - Accepted password for jmzhu from 112.96.33.40 port 48253 ssh2
      - >-
        Failed password for invalid user shengt from 115.233.91.242 port 49601
        ssh2
  - source_sentence: >-
      Unexpected error trying to delete block blk_9209337043266813528. BlockInfo
      not found in volumeMap.
    sentences:
      - >-
        Deleting block blk_6056040671227271408 file
        /mnt/hadoop/dfs/data/current/subdir63/blk_6056040671227271408
      - >-
        Unexpected error trying to delete block blk_8234858690572948833.
        BlockInfo not found in volumeMap.
      - >-
        [instance: 40568281-5a34-464a-b17b-99a0a5591045] Deleting instance files
        /var/lib/nova/instances/40568281-5a34-464a-b17b-99a0a5591045_del
  - source_sentence: >-
      writeBlock blk_5827639102770185153 received exception java.io.IOException:
      Could not read from stream
    sentences:
      - 'pam_unix(sshd:auth): check pass; user unknown'
      - >-
        Exception in receiveBlock for block blk_6495484866542253279
        java.io.EOFException
      - >-
        writeBlock blk_-3265479347842446682 received exception
        java.io.IOException: Could not read from stream
  - source_sentence: >-
      [instance: 71065aa4-40af-4e74-bd6a-ef77c7f4dd02] Total memory: 64172 MB,
      used: 512.00 MB
    sentences:
      - >-
        [instance: c6289e85-a048-42bd-b32a-427cc1b12ef5] Total memory: 64172 MB,
        used: 512.00 MB
      - >-
        [instance: 13b4689e-7f96-40a3-89a5-31d8e72a4113] VM Stopped (Lifecycle
        Event)
      - '[instance: 09e74992-da6d-4111-861e-6d22bbf91fdc] Claim successful'
pipeline_tag: sentence-similarity
library_name: sentence-transformers

SentenceTransformer based on sentence-transformers/multi-qa-mpnet-base-cos-v1

This is a sentence-transformers model finetuned from sentence-transformers/multi-qa-mpnet-base-cos-v1. 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/multi-qa-mpnet-base-cos-v1
Maximum Sequence Length: 512 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': 512, 'do_lower_case': False}) with Transformer model: 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("sentence_transformers_model_id")
# Run inference
sentences = [
    '[instance: 71065aa4-40af-4e74-bd6a-ef77c7f4dd02] Total memory: 64172 MB, used: 512.00 MB',
    '[instance: c6289e85-a048-42bd-b32a-427cc1b12ef5] Total memory: 64172 MB, used: 512.00 MB',
    '[instance: 09e74992-da6d-4111-861e-6d22bbf91fdc] Claim successful',
]
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]

Training Details

Training Dataset

Unnamed Dataset

Size: 33,174 training samples
Columns: sentence_0, sentence_1, and sentence_2

Approximate statistics based on the first 1000 samples:

	sentence_0	sentence_1	sentence_2
type	string	string	string
details	min: 12 tokens mean: 41.23 tokens max: 94 tokens	min: 12 tokens mean: 41.22 tokens max: 94 tokens	min: 12 tokens mean: 39.57 tokens max: 94 tokens

Samples:

sentence_0	sentence_1	sentence_2
`pam_unix(sshd:session): session opened for user hxu by (uid=0)`	`pam_unix(sshd:session): session opened for user curi by (uid=0)`	`Received disconnect from 58.218.213.45: 11: disconnect [preauth]`
`[instance: 78644035-9af0-4e94-b1bc-6412cb13e474] VM Stopped (Lifecycle Event)`	`[instance: 18473413-894b-4ae9-85eb-566134c89cd4] VM Stopped (Lifecycle Event)`	`[instance: 643b82e0-49dd-4ff5-a967-9483ba081678] Creating image`
`PAM 5 more authentication failures; logname= uid=0 euid=0 tty=ssh ruser= rhost=59.63.188.30 user=root`	`PAM 5 more authentication failures; logname= uid=0 euid=0 tty=ssh ruser= rhost=218.65.30.126 user=root`	`pam_unix(sshd:session): session opened for user hxu by (uid=0)`

Loss: TripletLoss with these parameters:

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

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 64
per_device_eval_batch_size: 64
multi_dataset_batch_sampler: round_robin

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: steps
prediction_loss_only: True
per_device_train_batch_size: 64
per_device_eval_batch_size: 64
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
num_train_epochs: 3
max_steps: -1
lr_scheduler_type: linear
lr_scheduler_kwargs: {}
warmup_ratio: 0.0
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: None
hub_always_push: False
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
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
average_tokens_across_devices: False
prompts: None
batch_sampler: batch_sampler
multi_dataset_batch_sampler: round_robin

Framework Versions

Python: 3.10.12
Sentence Transformers: 3.4.1
Transformers: 4.49.0
PyTorch: 2.6.0+cu124
Accelerate: 1.4.0
Datasets: 3.3.2
Tokenizers: 0.21.0

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}
}