---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:100
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
base_model: Snowflake/snowflake-arctic-embed-l
widget:
- source_sentence: What did the author plan to do with the dark meat and carcass after
cooking the turkey?
sentences:
- 'Let’s say a family of four wants to spend only $365 per month on groceries, saving
them $579 per month over that USDA average family in the link above. Investing
this savings would compound into about $102,483.00 every ten years, which would
obviously make a pretty big improvement in the financial health of the average
young family.
To hit a monthly grocery spending target like that, you first have to understand
what you are buying. There are four mouths to feed, each consuming three meals
a day or 91.25 meals per month. Let’s say they all need adult levels of calories,
so about 2000 per day.'
- When you eat beans and rice in the same meal, you’re getting complete protein
at virtually no cost. Nuts and especially peanut butter are also a good way to
mix high calories with built-in protein. Eggs contain the highest quality complete
protein of all (6 grams per egg), so I enjoy three of them every day.
- 'Turkey 101 Follow-up
Thought I’d share how my freezer “spring clean” is going. In an attempt to reduce
the number of trips to the grocery store in April, I’ve taken on the challenge
to use up what I have first. Here’s my first attempt at staying away from the
deli-counter:
Day 1- After anxiously awaiting the 3 day defrost, ready to cook turkey! Easy
enough. Since I usually overcook meat (just to make sure it’s dead), decided to
cook it breast side down; using gravity to my advantage, resulting in big, juicy
breasts (just like my hubby likes). Save dark meat for later. Freeze some white
meat, slice some for sandwiches, make broth from carcass.'
- source_sentence: What are the benefits of using whole oils in your diet according
to the context?
sentences:
- 'What to Eat
Finally, the fun part! As the wise people of India have proven beyond all other
cultures*, amazing food is all about preparation and spices, rather than starting
with costly ingredients. Once you know which ingredients make good staples, you
can easily poke around on the Internet or in any cookbook to find an infinite
number of good recipes that use them.
At the simplest “bachelor” level, you’ve got recipes like:
Fancy home fries:'
- 'Aha.. now things are sounding much better. Although not all of the foods above
cost less than $1 per meal, they can certainly average out to less than that,
depending on how you combine them. And when planning your menu to meet a certain
budget, averaging out is exactly your goal. You still want to be able to eat apples,
organic chicken breast, or whatever your heart desires. You just have to not eat
entirely those most expensive foods.
And remember, this $1.00 target is just something I picked out of a hat for an
example – you’re allowed to spend whatever works for you.'
- Whole oils are the ultimate example. They are packed with tasty, slow-metabolizing
calories, extremely good for you, and easy to mix into your diet. Using olive
oil as an example, you can one third of a day worth of calories for 57 cents.
Every time you dump these oils into a frying pan, or mix them into a recipe or
a salad dressing, you’re lowering your food cost – the oil provides calories that
your body might otherwise get from cans of Coke, Filet Mignon, or Burger King
dollar menu burgers.
- source_sentence: What ingredients did the "Master Mix" consist of, and how was it
used in cooking?
sentences:
- 'Day 4- Morph yesterdays’ meal into a turkey pot pie. Thankfully, pie crust does
not require yeast….I think. Decide to skip the 99 cent pre-packaged spice mix,
and make my own taco seasoning?! I don’t have any maltodextrin, modified corn
starch, autolyzed yeast extract, or caramel color (sulfites) in my cupboard; so
hope it turns out okay. Cook up the remaining meat for turkey tacos, and freeze
half for later.
Day 5- Enjoy eating leftovers.'
- This is a fantastic article. I’m generally responsible for our family’s grocery
shopping since I do the dinner cooking. Our budget is $185 for a family of four
per two weeks (two boys are almost 4 and 16 months). Some two-weeks are tight,
but it’s been worthwhile for our bottom line to keep the budget set. We also
budget $20 for restaurants per 2 weeks. Yes, I know we can’t go out on that,
but if we save it up, we can go out once a month or so, or order pizza one week,
or some combination. I’m sure our budget will increase when the boys get older,
but by then, we should be bringing in more money, so we plan on being able to
absorb the increase. Eating healthy and abundantly doesn’t have to be expensive,
but it does require work and
- 'When I was growing up, my parents had 9 mouths to feed, and I remember my mom
making something called a “Master Mix”. It was basically a biscuit mix with the
butter mixed in already, which she kept in a 4-liter ice cream pail. She’d use
it to make pizza dough (among other things), and she’d top it with canned tomato
soup (still condensed), shredded carrots and broccoli and cheddar cheese. My
siblings and I have confessed an occasional desire to eat it again, although I
don’t know I’d ever try it out on my own kids.
Reply
Diane
April 9, 2020, 11:30 pm'
- source_sentence: What changes were made to the homeowners insurance policy to achieve
a $600 reduction?
sentences:
- 'And contrary to the 1990s low-fat-diet fad, the human body loves oil. It’s yummy,
clean-burning, good for a giant range of body functions, and it is satisfying
to eat too. I eat a fairly high-fat/low-carb diet these days, yet I’m leaner than
ever, because the oily food doesn’t cause spikes of fake appetite like bread does.
I’ve even been known to bring containers of herb-infused olive oil on road trips,
supplementing every meal with this supercharger nutrient, especially when it’s
time for an extreme hike or a high-energy work day.
See Article: The Amazing Waist-Slimming, Wallet-Fattening Nutrient'
- First thing- reduced insurance by $600 with increasing the homeowners deductible
from $500 to $1000, and switching providers. Be warned- was not informed about
the “unannounced 3rd party” that would be knocking on my door, as well as the
additional cost to reappraise some items- but still overall a reduction. Second-
dropped the gym membership ($131/month). Now don’t have to feel guilty about not
going. Enjoy the outdoors more anyhow. Third- scaled back on vacation. I’m actually
“on vacation” everyday, as even with all the expenses, we’re at FI.
- 'Reply
beachmama
January 31, 2017, 11:39 am
As a 25+ year veg, 12 year vegan, I’ve always supplemented b-12. After getting
blood work done I found I was critically low in D3. Turns out it’s not just because
I’m a woman over 50 (now 61) and through menopause, or that I’ve been veg for
over half my life, I’m fit and walk the beach 20 miles a week so getting sun isn’t
enough even in California. Apparently most people are D3 deficient but never know
until they become symptomatic or have a blood test. I recommend you get a simple
test to check on b-12 and d3 just to make sure you’re in good shape. And you are
SO right about protein . . . Westerners eat FAR too much protein ; )
Reply
riley
March 29, 2012, 7:07 am'
- source_sentence: What additional ingredients are suggested to increase protein content
in the context?
sentences:
- 'Those are just two simple recipes. The key to frugal eating is to have at least
ten good things you know how to make.
There are many chefs among the readers. Maybe we will get to hear some of their
best low-cost and easy-to-make creations in the comments section below?
Further Reading:
Grocery Shopping with your Middle Finger – an old MMM classic on this same topic,
where I first started thinking about cost per calorie. But there I was dealing
with food stockups and sales rather than thinking of it on a per-meal or per-month
basis.
* According to the strong opinion of my own taste buds'
- 'Thanks for this timely article! In the midst of the March Challenge; was trying
to determine the next item to tackle- and groceries was it! How’d you know it
was $1000? Hmmm….psychic.
I FINALLY updated all the spending on Quicken last month to make myself stare
it in the face. No surprises; not ugly, but not very pretty either. The most valuable
outcome of the exercise was showing my husband that his hard efforts are appreciated,
and I’m stepping up!'
- cocoa and maybe some ground flax or whatever is lying around) for an extra 40
grams of protein.
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- cosine_accuracy@1
- cosine_accuracy@3
- cosine_accuracy@5
- cosine_accuracy@10
- cosine_precision@1
- cosine_precision@3
- cosine_precision@5
- cosine_precision@10
- cosine_recall@1
- cosine_recall@3
- cosine_recall@5
- cosine_recall@10
- cosine_ndcg@10
- cosine_mrr@10
- cosine_map@100
model-index:
- name: SentenceTransformer based on Snowflake/snowflake-arctic-embed-l
results:
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: Unknown
type: unknown
metrics:
- type: cosine_accuracy@1
value: 0.7582417582417582
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.9120879120879121
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.945054945054945
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9725274725274725
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.7582417582417582
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.304029304029304
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.18901098901098898
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09725274725274723
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.7582417582417582
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.9120879120879121
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.945054945054945
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9725274725274725
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.870936179086928
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.837580673294959
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.8395868579934513
name: Cosine Map@100
- type: cosine_accuracy@1
value: 0.66
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.76
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.88
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.66
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.2533333333333333
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.17599999999999993
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.08999999999999998
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.66
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.76
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.88
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.7735850437783321
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.7328571428571431
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.7377450230928493
name: Cosine Map@100
---
# SentenceTransformer based on Snowflake/snowflake-arctic-embed-l
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Snowflake/snowflake-arctic-embed-l](https://huggingface.co/Snowflake/snowflake-arctic-embed-l). It maps sentences & paragraphs to a 1024-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:** [Snowflake/snowflake-arctic-embed-l](https://huggingface.co/Snowflake/snowflake-arctic-embed-l)
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 1024 dimensions
- **Similarity Function:** Cosine Similarity
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, '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:
```bash
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("sentence_transformers_model_id")
# Run inference
sentences = [
'What additional ingredients are suggested to increase protein content in the context?',
'cocoa and maybe some ground flax or whatever is lying around) for an extra 40 grams of protein.',
'Thanks for this timely article! In the midst of the March Challenge; was trying to determine the next item to tackle- and groceries was it! How’d you know it was $1000? Hmmm….psychic.\nI FINALLY updated all the spending on Quicken last month to make myself stare it in the face. No surprises; not ugly, but not very pretty either. The most valuable outcome of the exercise was showing my husband that his hard efforts are appreciated, and I’m stepping up!',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
## Evaluation
### Metrics
#### Information Retrieval
* Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.7582 |
| cosine_accuracy@3 | 0.9121 |
| cosine_accuracy@5 | 0.9451 |
| cosine_accuracy@10 | 0.9725 |
| cosine_precision@1 | 0.7582 |
| cosine_precision@3 | 0.304 |
| cosine_precision@5 | 0.189 |
| cosine_precision@10 | 0.0973 |
| cosine_recall@1 | 0.7582 |
| cosine_recall@3 | 0.9121 |
| cosine_recall@5 | 0.9451 |
| cosine_recall@10 | 0.9725 |
| **cosine_ndcg@10** | **0.8709** |
| cosine_mrr@10 | 0.8376 |
| cosine_map@100 | 0.8396 |
#### Information Retrieval
* Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.66 |
| cosine_accuracy@3 | 0.76 |
| cosine_accuracy@5 | 0.88 |
| cosine_accuracy@10 | 0.9 |
| cosine_precision@1 | 0.66 |
| cosine_precision@3 | 0.2533 |
| cosine_precision@5 | 0.176 |
| cosine_precision@10 | 0.09 |
| cosine_recall@1 | 0.66 |
| cosine_recall@3 | 0.76 |
| cosine_recall@5 | 0.88 |
| cosine_recall@10 | 0.9 |
| **cosine_ndcg@10** | **0.7736** |
| cosine_mrr@10 | 0.7329 |
| cosine_map@100 | 0.7377 |
## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 100 training samples
* Columns: sentence_0 and sentence_1
* Approximate statistics based on the first 100 samples:
| | sentence_0 | sentence_1 |
|:--------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
| type | string | string |
| details | - min: 8 tokens
- mean: 16.78 tokens
- max: 35 tokens
| - min: 13 tokens
- mean: 132.1 tokens
- max: 195 tokens
|
* Samples:
| sentence_0 | sentence_1 |
|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| What is the significance of the date Mar 29, 2012, in relation to grocery expenses? | Killing your $1000 Grocery Bill
Home
Media
Contact
Email
RSS
Start Here
About
Random
MMM Recommends
Forum
MMM Classics
Mr. Money Mustache
View: Fancy Magazine | Classic Blog
Mar 29, 2012
428 comments
Killing your $1000 Grocery Bill |
| Wut do u think about spendin eighty dolars a week on food for a family? | “Eighty dollars a week on food for the three of you? That’s IT??”, said a friend, “We spend more than three times that amount!!”
“Whoa”, I replied, “I guess I’m not as spendy as I thought”.
Of course, the person telling me about her high food bill was more of a typical high-income spender in many ways. Her family also took out loans to buy new cars, had at least one $2500 carbon fiber road bike gleaming in the garage, and hired out the household chores to allow them to conveniently work a double-career-with-kids while still taking plenty of short vacations involving air travel. Looking back, I probably could have predicted a non-Mustachian grocery bill. |
| What factors contribute to the varying cost of living in the United States, and how can individuals make choices to manage their spending effectively? | But the experience still reminded me of the amazing variety of spending levels we all have available to us here in the United States. It is simultaneously one of the cheapest industrialized countries in the world to live in, and the most expensive. It all depends on the choices you make in your shopping, because everything in the world is available right here for your buying convenience. |
* Loss: [MatryoshkaLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
```json
{
"loss": "MultipleNegativesRankingLoss",
"matryoshka_dims": [
768,
512,
256,
128,
64
],
"matryoshka_weights": [
1,
1,
1,
1,
1
],
"n_dims_per_step": -1
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 10
- `per_device_eval_batch_size`: 10
- `num_train_epochs`: 5
- `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`: 10
- `per_device_eval_batch_size`: 10
- `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`: 5
- `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