This is a sentence-transformers model finetuned from sentence-transformers/all-MiniLM-L6-v2. 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: sentence-transformers/all-MiniLM-L6-v2
Maximum Sequence Length: 256 tokens
Output Dimensionality: 384 tokens
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': 256, 'do_lower_case': False}) with Transformer model: 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.

python

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("Nashhz/SBERT_KFOLD_Job_Descriptions_to_Skills")
# Run inference
sentences = [
    "Description: I'm seeking an expert in Google Sheets and data management to create a comprehensive tracking system for student progress. Details - Each student will have their own Google Sheet file. - Each file will contain 6 levels as separate sheets and a checkbox in each sheet for tracking progress. - When the checkbox is ticked, the data needs to be sent to a central database for us to know the student has completed a level and certificates need to be printed. The data to be sent to the database includes - Student's Name - Current Level - Package Details - Date and time Ideal skills for this project include - Advanced knowledge of Google Sheets - Experience with data management and database creation - Attention to detail to ensure accurate tracking of each student's progress. Each student's Google Sheet should include - Their Name - Their Current Level - Details about the Package they are on - A space to track their Progress Please only apply if you have relevant experience and can demonstrate your ability to deliver this project efficiently.",
    'Skills: PHP, Visual Basic, Data Processing, Data Entry, Excel',
    'Skills: Computer Security, Network Administration, Virtual Machines, Web Security, Linux',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

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

Training Details

Training Dataset

Unnamed Dataset

Size: 8,117 training samples
Columns: sentence_0, sentence_1, and label
Approximate statistics based on the first 1000 samples:
sentence_0 sentence_1 label
type string string float
details
min: 7 tokens
mean: 139.48 tokens
max: 256 tokens
min: 4 tokens
mean: 16.81 tokens
max: 33 tokens
min: -0.07
mean: 0.46
max: 0.83

Samples:

sentence_0	sentence_1	label
Description: Looking for a Freelance Videographer & Post-Production Editor! We're hosting a charity event near Sandton, Johannesburg, in support of those affected by abuse. The event will run for about 1-2 hours and features a live band performance. Project Scope Event Recording Capture the entire live set approx. 30 minutes including breaks and speakers, totaling around 1 hour. Post-Production Create a dynamic video for social media, similar to a movie trailer, highlighting the live band and key moments from the event. Photography Take a few impactful photos during the event, including post-production edits. Interviews Film one-on-one segments with speakers for inclusion in the post-event video. Sound Design Incorporate music and sound effects, using creative content available online. Delivery Deadline Edited photos and videos need to be submitted by 14 October 2024. This project aims to capture the spirit of the event, supporting the Family Protection Association login to view URL and empowering women while raising funds and awareness for the cause. If you're interested and have a flair for storytelling through video, please reach out! Date 13 October 2024 Time 1300pm to 1400pm 2 hour set	`Skills: Video Editing, Video Production, Video Services, Videography, After Effects`	`0.4115103483200073`
Description: Hi! I am Lradon from Andvids. We are a video production agency from China assisting our clients in finding content creators to produce unboxing videos. General requirements of the videos Video duration 1-3 minutes,without music Format Landscape screen 169 MP4 Content â' show your face to explain product features and demonstrate in English fluently. 30 of the time is used to explain product features and show product details, 70 of the time is used to demonstrate the use of the product and the use process in mutiple sences. â'Don't talking about price, personal privacy information, do not appear two-dimensional code, express bill, license plate, door plate, etc Clarity 1080p. Make sure the environment is clean and bright ,and the lens is stable and does not shake Upload to Amazon Sometimes we need you to upload the videos to Amazon If you are interested in this job,feel free to contact me and please send me an introduction video or anything you have shot login to view URL forward to receiving your login to view URL you!	`Skills: Video Editing, Video Production, Videography, Video Services, After Effects`	`0.4927669167518616`
Description: I'm looking for an expert in electronic circuit board design to create and manufacture a simple electronic board for industrial marine machinery. The ideal candidate should have - Experience in designing circuit boards - Ability to design simple, yet effective electronic boards. - Skills in both design and manufacturing of circuit boards. This project is all about creating a reliable, efficient circuit board that can withstand the rigors of marine use. The board is very straight forward design which will be- dc power supply covering 12 volt or 24 volt dc- but range with charging should cover 08 volts to 32 volts dc- it will have an on off button- when selected to on it will engage a 12 vdc solenoid very small and will activate it for 3 minutes and then when stopped it will do this every 7 days on a timer for 3 minutes- when turned off it will not activate and when turned on again it will start again it will activate the solid for 3 minutes and then when finish it will start a 7 day time to repeat the 3 minute solenoid and will be on constant repeat	`Skills: Electronics, Electrical Engineering, PCB Layout, Circuit Design, Engineering`	`0.2869749069213867`

Loss: CosineSimilarityLoss with these parameters:
json
1{ 2 "loss_fct": "torch.nn.modules.loss.MSELoss" 3}

Training Hyperparameters

Non-Default Hyperparameters

per_device_train_batch_size: 16
per_device_eval_batch_size: 16
num_train_epochs: 4
multi_dataset_batch_sampler: round_robin

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: no
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: 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: 4
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: 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
batch_sampler: batch_sampler
multi_dataset_batch_sampler: round_robin

Training Logs

Epoch	Step	Training Loss
0.9843	500	0.0012
1.9685	1000	0.0011
2.9528	1500	0.0008
3.9370	2000	0.0006
0.9843	500	0.0009
1.9685	1000	0.0008
2.9528	1500	0.0006
3.9370	2000	0.0005
0.9843	500	0.0007
1.9685	1000	0.0007
2.9528	1500	0.0005
3.9370	2000	0.0004
0.9843	500	0.0006
1.9685	1000	0.0006
2.9528	1500	0.0004
3.9370	2000	0.0003
0.9843	500	0.0005
1.9685	1000	0.0005
2.9528	1500	0.0004
3.9370	2000	0.0003

Framework Versions

Python: 3.12.6
Sentence Transformers: 3.2.0
Transformers: 4.45.2
PyTorch: 2.4.1+cpu
Accelerate: 1.0.1
Datasets: 3.0.1
Tokenizers: 0.20.1

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",
}

	sentence_0	sentence_1	label
type	string	string	float
details	min: 7 tokens mean: 139.48 tokens max: 256 tokens	min: 4 tokens mean: 16.81 tokens max: 33 tokens	min: -0.07 mean: 0.46 max: 0.83