Model: geetach/legal-ft-a201f63a-cb7a-4d10-aa78-6229827dff89
Author: geetach
Pipeline: sentence-similarity
Library: sentence-transformers
Tags: sentence-transformers, safetensors, bert, sentence-similarity, feature-extraction, generated_from_trainer, dataset_size:156, loss:MatryoshkaLoss, loss:MultipleNegativesRankingLoss, arxiv:1908.10084, arxiv:2205.13147, arxiv:1705.00652, base_model:Snowflake/snowflake-arctic-embed-l, base_model:finetune:Snowflake/snowflake-arctic-embed-l, model-index, text-embeddings-inference, endpoints_compatible, region:us
Base models (finetune): id: Snowflake/snowflake-arctic-embed-l
Card metadata: tags: sentence-transformers, sentence-similarity, feature-extraction, generated_from_trainer, dataset_size:156, loss:MatryoshkaLoss, loss:MultipleNegativesRankingLoss
base_model: Snowflake/snowflake-arctic-embed-l
widget: source_sentence: What are the numerical values associated with the tags "ai" and "generative-ai" in the context?  
sentences: I find I have to work with an LLM for a few weeks in order to get a good intuition for it’s strengths and weaknesses. This greatly limits how many I can evaluate myself!
The most frustrating thing for me is at the level of individual prompting.
Sometimes I’ll tweak a prompt and capitalize some of the words in it, to emphasize that I really want it to OUTPUT VALID MARKDOWN or similar. Did capitalizing those words make a difference? I still don’t have a good methodology for figuring that out.
We’re left with what’s effectively Vibes Based Development. It’s vibes all the way down.
I’d love to see us move beyond vibes in 2024!
LLMs are really smart, and also really, really dumb, blogging
            105


            ai
            1260


            generative-ai
            1087


            llms
            1074

Next: Tom Scott, and the formidable power of escalating streaks
Previous: Last weeknotes of 2023


 
 


Colophon
©
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025, blogging
            105


            ai
            1260


            generative-ai
            1087


            llms
            1074

Next: Tom Scott, and the formidable power of escalating streaks
Previous: Last weeknotes of 2023


 
 


Colophon
©
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025, source_sentence: Why are LLM use-cases involving long inputs considered more interesting than those relying solely on short prompts?
sentences: If you think about what they do, this isn’t such a big surprise. The grammar rules of programming languages like Python and JavaScript are massively less complicated than the grammar of Chinese, Spanish or English.
It’s still astonishing to me how effective they are though.
One of the great weaknesses of LLMs is their tendency to hallucinate—to imagine things that don’t correspond to reality. You would expect this to be a particularly bad problem for code—if an LLM hallucinates a method that doesn’t exist, the code should be useless., Longer inputs dramatically increase the scope of problems that can be solved with an LLM: you can now throw in an entire book and ask questions about its contents, but more importantly you can feed in a lot of example code to help the model correctly solve a coding problem. LLM use-cases that involve long inputs are far more interesting to me than short prompts that rely purely on the information already baked into the model weights. Many of my tools were built using this pattern., The boring yet crucial secret behind good system prompts is test-driven development. You don’t write down a system prompt and find ways to test it. You write down tests and find a system prompt that passes them.

It’s become abundantly clear over the course of 2024 that writing good automated evals for LLM-powered systems is the skill that’s most needed to build useful applications on top of these models. If you have a strong eval suite you can adopt new models faster, iterate better and build more reliable and useful product features than your competition.
Vercel’s Malte Ubl:, source_sentence: How is the author applying a similar pattern to the Chatbot Arena feature in their Datasette project?
sentences: In 2024, almost every significant model vendor released multi-modal models. We saw the Claude 3 series from Anthropic in March, Gemini 1.5 Pro in April (images, audio and video), then September brought Qwen2-VL and Mistral’s Pixtral 12B and Meta’s Llama 3.2 11B and 90B vision models. We got audio input and output from OpenAI in October, then November saw SmolVLM from Hugging Face and December saw image and video models from Amazon Nova.
In October I upgraded my LLM CLI tool to support multi-modal models via attachments. It now has plugins for a whole collection of different vision models., Then in December, the Chatbot Arena team introduced a whole new leaderboard for this feature, driven by users building the same interactive app twice with two different models and voting on the answer. Hard to come up with a more convincing argument that this feature is now a commodity that can be effectively implemented against all of the leading models.
I’ve been tinkering with a version of this myself for my Datasette project, with the goal of letting users use prompts to build and iterate on custom widgets and data visualizations against their own data. I also figured out a similar pattern for writing one-shot Python programs, enabled by uv., Large Language Models
They’re actually quite easy to build
You can run LLMs on your own devices
Hobbyists can build their own fine-tuned models
We don’t yet know how to build GPT-4
Vibes Based Development
LLMs are really smart, and also really, really dumb
Gullibility is the biggest unsolved problem
Code may be the best application
The ethics of this space remain diabolically complex
My blog in 2023, source_sentence: What are some differing opinions people have about the value and impact of LLMs?
sentences: Law is not ethics. Is it OK to train models on people’s content without their permission, when those models will then be used in ways that compete with those people?
As the quality of results produced by AI models has increased over the year, these questions have become even more pressing.
The impact on human society in terms of these models is already huge, if difficult to objectively measure.
People have certainly lost work to them—anecdotally, I’ve seen this for copywriters, artists and translators.
There are a great deal of untold stories here. I’m hoping 2024 sees significant amounts of dedicated journalism on this topic.
My blog in 2023
Here’s a tag cloud for content I posted to my blog in 2023 (generated using Django SQL Dashboard):, I think this means that, as individual users, we don’t need to feel any guilt at all for the energy consumed by the vast majority of our prompts. The impact is likely neglible compared to driving a car down the street or maybe even watching a video on YouTube.
Likewise, training. DeepSeek v3 training for less than $6m is a fantastic sign that training costs can and should continue to drop.
For less efficient models I find it useful to compare their energy usage to commercial flights. The largest Llama 3 model cost about the same as a single digit number of fully loaded passenger flights from New York to London. That’s certainly not nothing, but once trained that model can be used by millions of people at no extra training cost., So far, I think they’re a net positive. I’ve used them on a personal level to improve my productivity (and entertain myself) in all sorts of different ways. I think people who learn how to use them effectively can gain a significant boost to their quality of life.
A lot of people are yet to be sold on their value! Some think their negatives outweigh their positives, some think they are all hot air, and some even think they represent an existential threat to humanity.
They’re actually quite easy to build
The most surprising thing we’ve learned about LLMs this year is that they’re actually quite easy to build., source_sentence: What are the two main categories of AI agents described in the context?  
sentences: The two main categories I see are people who think AI agents are obviously things that go and act on your behalf—the travel agent model—and people who think in terms of LLMs that have been given access to tools which they can run in a loop as part of solving a problem. The term “autonomy” is often thrown into the mix too, again without including a clear definition.
(I also collected 211 definitions on Twitter a few months ago—here they are in Datasette Lite—and had gemini-exp-1206 attempt to summarize them.)
Whatever the term may mean, agents still have that feeling of perpetually “coming soon”., Longer inputs dramatically increase the scope of problems that can be solved with an LLM: you can now throw in an entire book and ask questions about its contents, but more importantly you can feed in a lot of example code to help the model correctly solve a coding problem. LLM use-cases that involve long inputs are far more interesting to me than short prompts that rely purely on the information already baked into the model weights. Many of my tools were built using this pattern., This remains astonishing to me. I thought a model with the capabilities and output quality of GPT-4 needed a datacenter class server with one or more $40,000+ GPUs.
These models take up enough of my 64GB of RAM that I don’t run them often—they don’t leave much room for anything else.
The fact that they run at all is a testament to the incredible training and inference performance gains that we’ve figured out over the past year. It turns out there was a lot of low-hanging fruit to be harvested in terms of model efficiency. I expect there’s still more to come.
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: 0.9166666666666666
name: Cosine Accuracy@1
verified: no, type: cosine_accuracy@3
value: 1
name: Cosine Accuracy@3
verified: no, type: cosine_accuracy@5
value: 1
name: Cosine Accuracy@5
verified: no, type: cosine_accuracy@10
value: 1
name: Cosine Accuracy@10
verified: no, type: cosine_precision@1
value: 0.9166666666666666
name: Cosine Precision@1
verified: no, type: cosine_precision@3
value: 0.3333333333333333
name: Cosine Precision@3
verified: no, type: cosine_precision@5
value: 0.20000000000000004
name: Cosine Precision@5
verified: no, type: cosine_precision@10
value: 0.10000000000000002
name: Cosine Precision@10
verified: no, type: cosine_recall@1
value: 0.9166666666666666
name: Cosine Recall@1
verified: no, type: cosine_recall@3
value: 1
name: Cosine Recall@3
verified: no, type: cosine_recall@5
value: 1
name: Cosine Recall@5
verified: no, type: cosine_recall@10
value: 1
name: Cosine Recall@10
verified: no, type: cosine_ndcg@10
value: 0.9692441461309548
name: Cosine Ndcg@10
verified: no, type: cosine_mrr@10
value: 0.9583333333333334
name: Cosine Mrr@10
verified: no, type: cosine_map@100
value: 0.9583333333333334
name: Cosine Map@100
verified: no
Likes: 0 | All-time downloads: 6
Created: 2025-05-05 02:19:31