Model: rajatgupta99924/AIE6-S09-eca4bfc6-eb64-44a4-a71d-e09bf2b78f50
Author: rajatgupta99924
Pipeline: sentence-similarity
Library: sentence-transformers
Tags: sentence-transformers, safetensors, bert, sentence-similarity, feature-extraction, dense, generated_from_trainer, dataset_size:160, 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, dense, generated_from_trainer, dataset_size:160, loss:MatryoshkaLoss, loss:MultipleNegativesRankingLoss
base_model: Snowflake/snowflake-arctic-embed-l
widget: source_sentence: Why might ChatGPT's answers change as the date approaches the holidays?
sentences: There’s now a fascinating ecosystem of people training their own models on top of these foundations, publishing those models, building fine-tuning datasets and sharing those too.
The Hugging Face Open LLM Leaderboard is one place that tracks these. I can’t even attempt to count them, and any count would be out-of-date within a few hours.
The best overall openly licensed LLM at any time is rarely a foundation model: instead, it’s whichever fine-tuned community model has most recently discovered the best combination of fine-tuning data.
This is a huge advantage for open over closed models: the closed, hosted models don’t have thousands of researchers and hobbyists around the world collaborating and competing to improve them., On the one hand, we keep on finding new things that LLMs can do that we didn’t expect—and that the people who trained the models didn’t expect either. That’s usually really fun!
But on the other hand, the things you sometimes have to do to get the models to behave are often incredibly dumb.
Does ChatGPT get lazy in December, because its hidden system prompt includes the current date and its training data shows that people provide less useful answers coming up to the holidays?
The honest answer is “maybe”! No-one is entirely sure, but if you give it a different date its answers may skew slightly longer., Getting back to models that beat GPT-4: Anthropic’s Claude 3 series launched in March, and Claude 3 Opus quickly became my new favourite daily-driver. They upped the ante even more in June with the launch of Claude 3.5 Sonnet—a model that is still my favourite six months later (though it got a significant upgrade on October 22, confusingly keeping the same 3.5 version number. Anthropic fans have since taken to calling it Claude 3.6)., source_sentence: What significance did the year 2024 have in relation to the word "slop"?
sentences: Intuitively, one would expect that systems this powerful would take millions of lines of complex code. Instead, it turns out a few hundred lines of Python is genuinely enough to train a basic version!
What matters most is the training  data. You need a lot of data to make these things work, and the quantity and quality of the training data appears to be the most important factor in how good the resulting model is.
If you can gather the right data, and afford to pay for the GPUs to train it, you can build an LLM., The year of slop
2024 was the year that the word "slop" became a term of art. I wrote about this in May, expanding on this tweet by @deepfates:, On the other hand, as software engineers we are better placed to take advantage of this than anyone else. We’ve all been given weird coding interns—we can use our deep knowledge to prompt them to solve coding problems more effectively than anyone else can.
The ethics of this space remain diabolically complex
In September last year Andy Baio and I produced the first major story on the unlicensed training data behind Stable Diffusion.
Since then, almost every major LLM (and most of the image generation models) have also been trained on unlicensed data., source_sentence: Why does the author find large language models (LLMs) infuriating as a computer scientist and software engineer?
sentences: Stuff we figured out about AI in 2023





















Simon Willison’s Weblog
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Stuff we figured out about AI in 2023
31st December 2023
2023 was the breakthrough year for Large Language Models (LLMs). I think it’s OK to call these AI—they’re the latest and (currently) most interesting development in the academic field of Artificial Intelligence that dates back to the 1950s.
Here’s my attempt to round up the highlights in one place!, The May 13th announcement of GPT-4o included a demo of a brand new voice mode, where the true multi-modal GPT-4o (the o is for “omni”) model could accept audio input and output incredibly realistic sounding speech without needing separate TTS or STT models.
The demo also sounded conspicuously similar to Scarlett Johansson... and after she complained the voice from the demo, Skye, never made it to a production product.
The delay in releasing the new voice mode after the initial demo caused quite a lot of confusion. I wrote about that in ChatGPT in “4o” mode is not running the new features yet., Still, I’m surprised that no-one has beaten the now almost year old GPT-4 by now. OpenAI clearly have some substantial tricks that they haven’t shared yet.
Vibes Based Development
As a computer scientist and software engineer, LLMs are infuriating.
Even the openly licensed ones are still the world’s most convoluted black boxes. We continue to have very little idea what they can do, how exactly they work and how best to control them.
I’m used to programming where the computer does exactly what I tell it to do. Prompting an LLM is decidedly not that!
The worst part is the challenge of evaluating them.
There are plenty of benchmarks, but no benchmark is going to tell you if an LLM actually “feels” right when you try it for a given task., source_sentence: How did Google’s NotebookLM enhance audio output in its September release?
sentences: Your browser does not support the audio element.

OpenAI aren’t the only group with a multi-modal audio model. Google’s Gemini also accepts audio input, and the Google Gemini apps can speak in a similar way to ChatGPT now. Amazon also pre-announced voice mode for Amazon Nova, but that’s meant to roll out in Q1 of 2025.
Google’s NotebookLM, released in September, took audio output to a new level by producing spookily realistic conversations between two “podcast hosts” about anything you fed into their tool. They later added custom instructions, so naturally I turned them into pelicans:


Your browser does not support the audio element., 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., I think people who complain that LLM improvement has slowed are often missing the enormous advances in these multi-modal models. Being able to run prompts against images (and audio and video) is a fascinating new way to apply these models.
Voice and live camera mode are science fiction come to life
The audio and live video modes that have started to emerge deserve a special mention.
The ability to talk to ChatGPT first arrived in September 2023, but it was mostly an illusion: OpenAI used their excellent Whisper speech-to-text model and a new text-to-speech model (creatively named tts-1) to enable conversations with the ChatGPT mobile apps, but the actual model just saw text., source_sentence: What type of dish is shown in the photo and what does it contain?
sentences: Against this photo of butterflies at the California Academy of Sciences:


A shallow dish, likely a hummingbird or butterfly feeder, is red.  Pieces of orange slices of fruit are visible inside the dish.
Two butterflies are positioned in the feeder, one is a dark brown/black butterfly with white/cream-colored markings.  The other is a large, brown butterfly with patterns of lighter brown, beige, and black markings, including prominent eye spots. The larger brown butterfly appears to be feeding on the fruit., Except... you can run generated code to see if it’s correct. And with patterns like ChatGPT Code Interpreter the LLM can execute the code itself, process the error message, then rewrite it and keep trying until it works!
So hallucination is a much lesser problem for code generation than for anything else. If only we had the equivalent of Code Interpreter for fact-checking natural language!
How should we feel about this as software engineers?
On the one hand, this feels like a threat: who needs a programmer if ChatGPT can write code for you?, On the other hand, as software engineers we are better placed to take advantage of this than anyone else. We’ve all been given weird coding interns—we can use our deep knowledge to prompt them to solve coding problems more effectively than anyone else can.
The ethics of this space remain diabolically complex
In September last year Andy Baio and I produced the first major story on the unlicensed training data behind Stable Diffusion.
Since then, almost every major LLM (and most of the image generation models) have also been trained on unlicensed data.
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: 0.95
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: 0.95
name: Cosine Precision@1
verified: no, type: cosine_precision@3
value: 0.33333333333333326
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: 0.95
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: 0.9815464876785729
name: Cosine Ndcg@10
verified: no, type: cosine_mrr@10
value: 0.975
name: Cosine Mrr@10
verified: no, type: cosine_map@100
value: 0.975
name: Cosine Map@100
verified: no
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Created: 2025-09-15 02:31:46