This is a sentence-transformers model finetuned from sentence-transformers/all-MiniLM-L6-v2 on the csv dataset. 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 dimensions
Similarity Function: Cosine Similarity
Training Dataset:
- csv

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, '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.

python

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("Ermiyas/embedding-1")
# Run inference
sentences = [
    " We're working on a story about negotiating a raise at work during a time of rising inflation. We need these queries answered for the story.\n\n1. What impact, if any, is skyrocketing inflation having on salaries and compensation on the job?\n\n2. Is it reasonable for an employee to ask for a raise at a time of high inflation? Or should job responsibilities be the only factor when asking for a raise?\n\n3. What are your best tips for negotiating a raise at a time of high inflation? What strategies works and what doesn't?\n\nThanks.  |  Looking for negotiation, career and financial experts for an article on negotiating a raise at work at a time of high inflation ",
    ' ',
    'Hey Amy Walters, Thank you for reaching out. Dr. Sandeep Nayak, a Surgical Oncologist (DNB, MRCS UK, Fellowship in Laparoscopic and Robotic Surgical Oncology), provides his professional insight into the claim of curing cancer through dietary changes: "A claim like this needs to be examined carefully. While diet and nutrition play a crucial role in overall health and can support recovery during and after cancer treatment, there is no scientific evidence to suggest that dietary changes alone can cure cancer. The case mentioned, where surgery was performed and a plant-based diet followed, likely reflects the combined effect of medical intervention and lifestyle improvements. Surgery is often a curative treatment for localized cancers, but attributing the cure solely to dietary changes is misleading. What diet does offer is an enhancement to the body’s ability to heal and recover. For example, a diet rich in vegetables, legumes, and whole foods provides antioxidants and anti-inflammatory properties that can support the immune system and potentially reduce the risk of cancer recurrence. However, this should complement, not replace, proven cancer therapies like surgery, chemotherapy, or immunotherapy. It’s important for patients to understand that cancer is multifactorial, involving genetic, environmental, and lifestyle factors. While maintaining a healthy diet is essential for reducing risks and supporting recovery, it is not a standalone cure. Bold claims like this can deter patients from seeking evidence-based treatments, which could have life-saving outcomes." Dr. Nayak emphasizes that diet, while a powerful tool in promoting health, is one part of a larger treatment plan. "The best approach combines advanced medical treatments with lifestyle modifications for long-term well-being and prevention. Patients should always consult their oncologist or healthcare provider before making decisions about cancer care based on anecdotal reports." If you’d like additional commentary or specific examples, please feel free to reach out. Here are Dr. Nayak’s details for attribution: Dr. Sandeep Nayak DNB (General Surgery), DNB (Surgical Oncology), MRCS (UK), MNAMS (General Surgery) Fellowship in Laparoscopic and Robotic Surgical Oncology Profile: Dr. Sandeep Nayak Website: MACS for Cancer',
]
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,     -0.0021,      0.4980],
#         [    -0.0021,      1.0000,      0.0003],
#         [     0.4980,      0.0003,      1.0000]])

Evaluation

Metrics

Semantic Similarity

Dataset: sts-dev
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.9238
spearman_cosine	0.8171

Training Details

Training Dataset

csv

Dataset: csv
Size: 1,900 training samples
Columns: request, pitch, and score
Approximate statistics based on the first 1000 samples:
request pitch score
type string string float
details
min: 16 tokens
mean: 120.15 tokens
max: 256 tokens
min: 2 tokens
mean: 160.7 tokens
max: 256 tokens
min: 0.0
mean: 0.75
max: 85.0

Samples:

request	pitch	score
`What can be done to improve diversity for financial advisors?`	What are tangible ways that the financial advisory industry can increase diversity?	What can be done to improve diversity for financial advisors? What are tangible ways that the financial advisory industry can increase diversity?[object Object][object Object]Even as an eternal optimist, I'll start with the business case: diverse teams are more likely to outperform on the bottom line. McKinsey can give you the stats. With that out of the way, let's talk about action. "Diversity & Inclusion" is often taken to mean hiring statistics, but increasing diversity in the financial advisory industry requires work to build a talent pipeline for advisors and to build better services from advisors. A history of exclusionary practices has resulted in our status quo; today, financial advisors are often assumed to be well-off themselves and to serve a well-off clientele. Changing this history is going to take time, and improving diversity for financial advisors and for the industry won't happen overnight. [object Object][object Object]The industry should start by investing time to build a diverse customer base and to meet their needs. On ...
`UPDATE: Medical Doctors who are also Geriatricians`	Topic: Baby Boomers I want to speak with US medical experts (for 15 min) support the medical needs of those born from 1946 and 1964 AKA "Boomers" Previous: or those in professions that support their healthcare and retirement concerns. Aging life Specialists and Care Managers and Geriatricians to the front, please. Someone who can chat with me about the nuance in this demographic and what that are currently facing. -Please don't send ready-made quotes for this one. -Looking for experts who can speak to the health and social concerns of this demographic. It would be ideal to connect with an expert who can speak for 15 minutes on the 11 or 12 of next week. Here are my primary questions about Baby Boomers: What Role do Baby Boomers play in family dynamics today and why does it matter? What are challenges baby boomers are facing when it comes to their health? What are challenges baby boomers are facing when it comes to senio...	Hi Yolande,[object Object][object Object]I'd like to introduce you to Ann Lilly as a potential source for you. Ann is the Brand Lead for House Doctors, a leading home improvement and handyman service, and just last year, she helped launch the Aging in Place program. This program prioritizes fall prevention for the aging population, helping so seniors can live independently and safely in their homes with a solution that addresses their unique needs.[object Object][object Object]One in Four senior citizens in the U.S. suffer a fall each year. With nearly 90% of seniors expressing a desire to remain in their own homes as they age, Ann is helping to ensure that seniors can continue living in their homes with the comfort and safety they deserve.[object Object][object Object]KEY STATS:[object Object]• 1 in 4 seniors in the U.S. falls each year[object Object]• Falls result in 3 million emergency department visits annually.[object Object]• 76% of remodelers have seen a significant or moderate increase in requests for aging-in-place features in the last five years.[object Object]• The most common aging-i...
`Looking to speak to a researcher that has been in academia 20+ years`	Looking to have a quick conversation with a researcher that has been in academia 20+ years and uses Twitter to share their research.

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

Evaluation Dataset

csv

Dataset: csv
Size: 316 evaluation samples
Columns: request, pitch, and score
Approximate statistics based on the first 316 samples:
request pitch score
type string string float
details
min: 14 tokens
mean: 120.31 tokens
max: 256 tokens
min: 2 tokens
mean: 161.22 tokens
max: 256 tokens
min: 0.0
mean: 0.66
max: 0.95

Samples:

request	pitch	score
I am working on an article for USA Today's Blueprint (Business Section) on the best onboarding practices. Looking for an HR professional to weigh in on the following: [object Object][object Object]1. What are some top onboarding practices? For instance, keep new hires engaged before their joining date, set up team meet and greets, set clear goals and objectives, check in often, collect feedback to refine onboarding practices. [object Object][object Object]2. Why is it important to implement these best onboarding practices? How does it help with productivity, retention, general morale and well-being in the workplace? [object Object][object Object]3. What are some challenges to look out for during onboarding? [object Object][object Object]4. What are some tools to help with the onboarding process?	Human resources professional needed for insights on best onboarding and worker retention practices	Hey Jackie, I work with Keith Harper, the founder and CEO of NY-based Above & Beyond Talent Acquisition. A&B is on a mission to increase executive diversity among the Fortune 500 and along the way Keith has helped hundreds of companies find and successfully onboard candidates. They actually covered some onboarding strategies that maximize retention in a recent blog post here: [object Object] I think Keith would be a fantastic resource and I'd be happy to connect you both if you'd like to schedule some time to speak with him. Very best, Bruce
I’m working on a TravelSavvy feature on the Best Spring Break Destinations for Families With Young Kids—places where parents can relax, kids can play, and everyone gets the most out of their vacation. Whether it's a beach escape with calm waters (for newbie swimmers), a resort packed with kid-friendly activities, or an off-the-beaten-path gem perfect that's safe for family adventures, I want to hear about it! If you rep a destination (hotel, resort, tourism board) that offers an unforgettable spring break experience for families, send me your pitch! Please include links to book, off-peak and peak price ranges, and details on what makes it kid-friendly! I'll respond to those I plan to include.	Best Spring Break Destinations for Families With Young Kids	Hi Ysolt, Hope you're doing well! For your story on the best Spring Break Destinations for Families with Young Kids, I wanted to put forward Cornwall, England for potential consideration. Destination: Cornwall, England Why: Cornwall is the ultimate British beach holiday location in the Spring as the weather gets noticeably warmer here much earlier than in other parts of Britain. The golden sand and turquoise water rivals that of the Caribbean. The quaint fishing villages and stunning beaches make for the perfect family break. During the summer months there will be ice cream on almost every corner, restaurants with delicious locally sourced fish dishes and coves to explore at low tide. It is beautiful corner of England that also offers fantastic car free biking trails (The Camel Trail) ideal for all ages and abilities. Coastal walks providing dramatic cliff tops, film locations from Poldark and more miles of sandy beaches for sandcastles or sunbathing! Quote from Gaby Cecil, Commercial ...
`Hi there, I'm looking for a telecoms engineer to discuss ways to connect to a satellite during a service outage, and share are more similar tips.`	Looking for a telecoms engineer	Hi Maria, Cool story you're working on here. I work with Benchmark Electronics, an advanced manufacturing and engineering company based in Arizona. They do A LOT of telecom work ([object Object]) and I'd love to find an engineer for you to speak with there. I have one question for you on this though, is the listed deadline you have for collecting sources or is it for getting answers to the questions you have? If its the former, I can package these up for some folks and work to get them to you but if its the latter the turn around may be too tight. Please let me know either way.

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

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 32
per_device_eval_batch_size: 64
gradient_accumulation_steps: 2
learning_rate: 0.0001
num_train_epochs: 7
warmup_ratio: 0.2
load_best_model_at_end: True

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: steps
prediction_loss_only: True
per_device_train_batch_size: 32
per_device_eval_batch_size: 64
per_gpu_train_batch_size: None
per_gpu_eval_batch_size: None
gradient_accumulation_steps: 2
eval_accumulation_steps: None
torch_empty_cache_steps: None
learning_rate: 0.0001
weight_decay: 0.0
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
max_grad_norm: 1.0
num_train_epochs: 7
max_steps: -1
lr_scheduler_type: linear
lr_scheduler_kwargs: {}
warmup_ratio: 0.2
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: True
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: batch_sampler
multi_dataset_batch_sampler: proportional
router_mapping: {}
learning_rate_mapping: {}

Training Logs

Epoch	Step	Training Loss	Validation Loss	sts-dev_spearman_cosine
-1	-1	-	-	0.7685
0.3333	10	11.2084	0.0593	0.7714
0.6667	20	0.0316	0.0308	0.7794
1.0	30	0.0245	0.0334	0.7870
1.3333	40	0.0136	0.0246	0.7978
1.6667	50	11.0605	0.0233	0.8154
2.0	60	0.0155	0.0234	0.8161
2.3333	70	0.0125	0.0244	0.8331
2.6667	80	11.0853	0.0230	0.8264
3.0	90	0.0116	0.0225	0.8261
3.3333	100	0.0071	0.0226	0.8279
3.6667	110	11.0368	0.0227	0.8165
4.0	120	0.0072	0.0226	0.8145
4.3333	130	0.005	0.0222	0.8194
4.6667	140	0.0054	0.0225	0.8193
5.0	150	11.034	0.0226	0.8103
5.3333	160	0.0036	0.0225	0.8150
5.6667	170	0.0038	0.0228	0.8193
6.0	180	11.0309	0.0224	0.8168
6.3333	190	11.0297	0.0224	0.8163
6.6667	200	0.0029	0.0225	0.8165
7.0	210	0.0026	0.0225	0.8171

The bold row denotes the saved checkpoint.

Framework Versions

Python: 3.11.13
Sentence Transformers: 5.0.0
Transformers: 4.55.0
PyTorch: 2.6.0+cu124
Accelerate: 1.9.0
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",
}

	request	pitch	score
type	string	string	float
details	min: 16 tokens mean: 120.15 tokens max: 256 tokens	min: 2 tokens mean: 160.7 tokens max: 256 tokens	min: 0.0 mean: 0.75 max: 85.0