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--- |
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tags: |
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- sentence-transformers |
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- sentence-similarity |
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- feature-extraction |
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- generated_from_trainer |
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- dataset_size:156 |
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- loss:MatryoshkaLoss |
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- loss:MultipleNegativesRankingLoss |
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base_model: Snowflake/snowflake-arctic-embed-l |
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widget: |
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- source_sentence: According to the context, what is considered the biggest unsolved |
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problem related to the issues discussed? |
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sentences: |
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- 'The GPT-4 barrier was comprehensively broken |
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Some of those GPT-4 models run on my laptop |
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LLM prices crashed, thanks to competition and increased efficiency |
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Multimodal vision is common, audio and video are starting to emerge |
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Voice and live camera mode are science fiction come to life |
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Prompt driven app generation is a commodity already |
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Universal access to the best models lasted for just a few short months |
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“Agents” still haven’t really happened yet |
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Evals really matter |
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Apple Intelligence is bad, Apple’s MLX library is excellent |
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The rise of inference-scaling “reasoning” models |
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Was the best currently available LLM trained in China for less than $6m? |
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The environmental impact got better |
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The environmental impact got much, much worse' |
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- 'Sometimes it omits sections of code and leaves you to fill them in, but if you |
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tell it you can’t type because you don’t have any fingers it produces the full |
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code for you instead. |
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There are so many more examples like this. Offer it cash tips for better answers. |
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Tell it your career depends on it. Give it positive reinforcement. It’s all so |
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dumb, but it works! |
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Gullibility is the biggest unsolved problem |
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I coined the term prompt injection in September last year. |
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15 months later, I regret to say that we’re still no closer to a robust, dependable |
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solution to this problem. |
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I’ve written a ton about this already. |
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Beyond that specific class of security vulnerabilities, I’ve started seeing this |
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as a wider problem of gullibility.' |
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- 'Your browser does not support the audio element. |
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OpenAI aren’t the only group with a multi-modal audio model. Google’s Gemini also |
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accepts audio input, and the Google Gemini apps can speak in a similar way to |
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ChatGPT now. Amazon also pre-announced voice mode for Amazon Nova, but that’s |
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meant to roll out in Q1 of 2025. |
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Google’s NotebookLM, released in September, took audio output to a new level by |
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producing spookily realistic conversations between two “podcast hosts” about anything |
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you fed into their tool. They later added custom instructions, so naturally I |
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turned them into pelicans: |
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Your browser does not support the audio element.' |
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- source_sentence: What are the two main factors driving the recent price drops in |
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running prompts for LLMs? |
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sentences: |
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- 'These abilities are just a few weeks old at this point, and I don’t think their |
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impact has been fully felt yet. If you haven’t tried them out yet you really should. |
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Both Gemini and OpenAI offer API access to these features as well. OpenAI started |
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with a WebSocket API that was quite challenging to use, but in December they announced |
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a new WebRTC API which is much easier to get started with. Building a web app |
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that a user can talk to via voice is easy now! |
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Prompt driven app generation is a commodity already |
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This was possible with GPT-4 in 2023, but the value it provides became evident |
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in 2024.' |
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- 'These price drops are driven by two factors: increased competition and increased |
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efficiency. The efficiency thing is really important for everyone who is concerned |
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about the environmental impact of LLMs. These price drops tie directly to how |
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much energy is being used for running prompts. |
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There’s still plenty to worry about with respect to the environmental impact of |
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the great AI datacenter buildout, but a lot of the concerns over the energy cost |
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of individual prompts are no longer credible. |
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Here’s a fun napkin calculation: how much would it cost to generate short descriptions |
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of every one of the 68,000 photos in my personal photo library using Google’s |
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Gemini 1.5 Flash 8B (released in October), their cheapest model?' |
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- 'The two main categories I see are people who think AI agents are obviously things |
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that go and act on your behalf—the travel agent model—and people who think in |
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terms of LLMs that have been given access to tools which they can run in a loop |
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as part of solving a problem. The term “autonomy” is often thrown into the mix |
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too, again without including a clear definition. |
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(I also collected 211 definitions on Twitter a few months ago—here they are in |
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Datasette Lite—and had gemini-exp-1206 attempt to summarize them.) |
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Whatever the term may mean, agents still have that feeling of perpetually “coming |
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soon”.' |
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- source_sentence: What is the new approach to scaling models mentioned in the context? |
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sentences: |
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- 'Large Language Models |
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They’re actually quite easy to build |
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You can run LLMs on your own devices |
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Hobbyists can build their own fine-tuned models |
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We don’t yet know how to build GPT-4 |
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Vibes Based Development |
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LLMs are really smart, and also really, really dumb |
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Gullibility is the biggest unsolved problem |
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Code may be the best application |
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The ethics of this space remain diabolically complex |
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My blog in 2023' |
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- 'The biggest innovation here is that it opens up a new way to scale a model: instead |
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of improving model performance purely through additional compute at training time, |
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models can now take on harder problems by spending more compute on inference. |
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The sequel to o1, o3 (they skipped “o2” for European trademark reasons) was announced |
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on 20th December with an impressive result against the ARC-AGI benchmark, albeit |
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one that likely involved more than $1,000,000 of compute time expense! |
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o3 is expected to ship in January. I doubt many people have real-world problems |
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that would benefit from that level of compute expenditure—I certainly don’t!—but |
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it appears to be a genuine next step in LLM architecture for taking on much harder |
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problems.' |
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- 'On the other hand, as software engineers we are better placed to take advantage |
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of this than anyone else. We’ve all been given weird coding interns—we can use |
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our deep knowledge to prompt them to solve coding problems more effectively than |
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anyone else can. |
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The ethics of this space remain diabolically complex |
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In September last year Andy Baio and I produced the first major story on the unlicensed |
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training data behind Stable Diffusion. |
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Since then, almost every major LLM (and most of the image generation models) have |
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also been trained on unlicensed data.' |
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- source_sentence: What new feature did the Chatbot Arena team introduce in December, |
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and how is it evaluated? |
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sentences: |
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- 'Nothing yet from Anthropic or Meta but I would be very surprised if they don’t |
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have their own inference-scaling models in the works. Meta published a relevant |
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paper Training Large Language Models to Reason in a Continuous Latent Space in |
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December. |
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Was the best currently available LLM trained in China for less than $6m? |
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Not quite, but almost! It does make for a great attention-grabbing headline. |
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The big news to end the year was the release of DeepSeek v3—dropped on Hugging |
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Face on Christmas Day without so much as a README file, then followed by documentation |
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and a paper the day after that.' |
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- 'Prompt injection is a natural consequence of this gulibility. I’ve seen precious |
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little progress on tackling that problem in 2024, and we’ve been talking about |
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it since September 2022. |
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I’m beginning to see the most popular idea of “agents” as dependent on AGI itself. |
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A model that’s robust against gulliblity is a very tall order indeed. |
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Evals really matter |
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Anthropic’s Amanda Askell (responsible for much of the work behind Claude’s Character):' |
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- 'Then in December, the Chatbot Arena team introduced a whole new leaderboard for |
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this feature, driven by users building the same interactive app twice with two |
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different models and voting on the answer. Hard to come up with a more convincing |
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argument that this feature is now a commodity that can be effectively implemented |
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against all of the leading models. |
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I’ve been tinkering with a version of this myself for my Datasette project, with |
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the goal of letting users use prompts to build and iterate on custom widgets and |
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data visualizations against their own data. I also figured out a similar pattern |
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for writing one-shot Python programs, enabled by uv.' |
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- source_sentence: What are some potential negative uses of Large Language Models |
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as described in the context? |
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sentences: |
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- 'Here’s the sequel to this post: Things we learned about LLMs in 2024. |
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Large Language Models |
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In the past 24-36 months, our species has discovered that you can take a GIANT |
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corpus of text, run it through a pile of GPUs, and use it to create a fascinating |
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new kind of software. |
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LLMs can do a lot of things. They can answer questions, summarize documents, translate |
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from one language to another, extract information and even write surprisingly |
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competent code. |
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They can also help you cheat at your homework, generate unlimited streams of fake |
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content and be used for all manner of nefarious purposes.' |
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- 'Then there’s the rest. If you browse the Chatbot Arena leaderboard today—still |
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the most useful single place to get a vibes-based evaluation of models—you’ll |
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see that GPT-4-0314 has fallen to around 70th place. The 18 organizations with |
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higher scoring models are Google, OpenAI, Alibaba, Anthropic, Meta, Reka AI, 01 |
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AI, Amazon, Cohere, DeepSeek, Nvidia, Mistral, NexusFlow, Zhipu AI, xAI, AI21 |
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Labs, Princeton and Tencent. |
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Training a GPT-4 beating model was a huge deal in 2023. In 2024 it’s an achievement |
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that isn’t even particularly notable, though I personally still celebrate any |
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time a new organization joins that list. |
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Some of those GPT-4 models run on my laptop' |
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- '“Agents” still haven’t really happened yet |
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I find the term “agents” extremely frustrating. It lacks a single, clear and widely |
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understood meaning... but the people who use the term never seem to acknowledge |
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that. |
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If you tell me that you are building “agents”, you’ve conveyed almost no information |
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to me at all. Without reading your mind I have no way of telling which of the |
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dozens of possible definitions you are talking about.' |
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pipeline_tag: sentence-similarity |
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library_name: sentence-transformers |
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metrics: |
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- cosine_accuracy@1 |
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- cosine_accuracy@3 |
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- cosine_accuracy@5 |
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- cosine_accuracy@10 |
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- cosine_precision@1 |
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- cosine_precision@3 |
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- cosine_precision@5 |
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- cosine_precision@10 |
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- cosine_recall@1 |
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- cosine_recall@3 |
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- cosine_recall@5 |
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- cosine_recall@10 |
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- cosine_ndcg@10 |
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- cosine_mrr@10 |
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- cosine_map@100 |
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model-index: |
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- name: SentenceTransformer based on Snowflake/snowflake-arctic-embed-l |
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results: |
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- task: |
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type: information-retrieval |
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name: Information Retrieval |
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dataset: |
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name: Unknown |
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type: unknown |
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metrics: |
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- type: cosine_accuracy@1 |
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value: 0.9166666666666666 |
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name: Cosine Accuracy@1 |
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- type: cosine_accuracy@3 |
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value: 1.0 |
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name: Cosine Accuracy@3 |
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- type: cosine_accuracy@5 |
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value: 1.0 |
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name: Cosine Accuracy@5 |
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- type: cosine_accuracy@10 |
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value: 1.0 |
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name: Cosine Accuracy@10 |
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- type: cosine_precision@1 |
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value: 0.9166666666666666 |
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name: Cosine Precision@1 |
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- type: cosine_precision@3 |
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value: 0.3333333333333333 |
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name: Cosine Precision@3 |
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- type: cosine_precision@5 |
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value: 0.20000000000000004 |
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|
name: Cosine Precision@5 |
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- type: cosine_precision@10 |
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value: 0.10000000000000002 |
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name: Cosine Precision@10 |
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- type: cosine_recall@1 |
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value: 0.9166666666666666 |
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name: Cosine Recall@1 |
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- type: cosine_recall@3 |
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value: 1.0 |
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name: Cosine Recall@3 |
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- type: cosine_recall@5 |
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value: 1.0 |
|
|
name: Cosine Recall@5 |
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- type: cosine_recall@10 |
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value: 1.0 |
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name: Cosine Recall@10 |
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- type: cosine_ndcg@10 |
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value: 0.9692441461309548 |
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|
name: Cosine Ndcg@10 |
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|
- type: cosine_mrr@10 |
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|
value: 0.9583333333333334 |
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|
name: Cosine Mrr@10 |
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- type: cosine_map@100 |
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value: 0.9583333333333334 |
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name: Cosine Map@100 |
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--- |
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# SentenceTransformer based on Snowflake/snowflake-arctic-embed-l |
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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. |
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## Model Details |
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### Model Description |
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- **Model Type:** Sentence Transformer |
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- **Base model:** [Snowflake/snowflake-arctic-embed-l](https://huggingface.co/Snowflake/snowflake-arctic-embed-l) <!-- at revision d8fb21ca8d905d2832ee8b96c894d3298964346b --> |
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- **Maximum Sequence Length:** 512 tokens |
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- **Output Dimensionality:** 1024 dimensions |
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- **Similarity Function:** Cosine Similarity |
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<!-- - **Training Dataset:** Unknown --> |
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<!-- - **Language:** Unknown --> |
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<!-- - **License:** Unknown --> |
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### Model Sources |
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- **Documentation:** [Sentence Transformers Documentation](https://sbert.net) |
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- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) |
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- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) |
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### Full Model Architecture |
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``` |
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SentenceTransformer( |
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(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel |
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(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}) |
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(2): Normalize() |
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) |
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``` |
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## Usage |
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### Direct Usage (Sentence Transformers) |
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First install the Sentence Transformers library: |
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```bash |
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pip install -U sentence-transformers |
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``` |
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Then you can load this model and run inference. |
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```python |
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from sentence_transformers import SentenceTransformer |
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# Download from the 🤗 Hub |
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model = SentenceTransformer("rajkstats/legal-ft-c8dc4aca-bfc4-4433-81b2-4cc294d0eb93") |
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# Run inference |
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sentences = [ |
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'What are some potential negative uses of Large Language Models as described in the context?', |
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'Here’s the sequel to this post: Things we learned about LLMs in 2024.\nLarge Language Models\nIn the past 24-36 months, our species has discovered that you can take a GIANT corpus of text, run it through a pile of GPUs, and use it to create a fascinating new kind of software.\nLLMs can do a lot of things. They can answer questions, summarize documents, translate from one language to another, extract information and even write surprisingly competent code.\nThey can also help you cheat at your homework, generate unlimited streams of fake content and be used for all manner of nefarious purposes.', |
|
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'“Agents” still haven’t really happened yet\nI find the term “agents” extremely frustrating. It lacks a single, clear and widely understood meaning... but the people who use the term never seem to acknowledge that.\nIf you tell me that you are building “agents”, you’ve conveyed almost no information to me at all. Without reading your mind I have no way of telling which of the dozens of possible definitions you are talking about.', |
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] |
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embeddings = model.encode(sentences) |
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print(embeddings.shape) |
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# [3, 1024] |
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# Get the similarity scores for the embeddings |
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similarities = model.similarity(embeddings, embeddings) |
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print(similarities.shape) |
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# [3, 3] |
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``` |
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<!-- |
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### Direct Usage (Transformers) |
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<details><summary>Click to see the direct usage in Transformers</summary> |
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</details> |
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--> |
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<!-- |
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### Downstream Usage (Sentence Transformers) |
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You can finetune this model on your own dataset. |
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<details><summary>Click to expand</summary> |
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</details> |
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--> |
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<!-- |
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### Out-of-Scope Use |
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*List how the model may foreseeably be misused and address what users ought not to do with the model.* |
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--> |
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## Evaluation |
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### Metrics |
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#### Information Retrieval |
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* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) |
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| Metric | Value | |
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|:--------------------|:-----------| |
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| cosine_accuracy@1 | 0.9167 | |
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| cosine_accuracy@3 | 1.0 | |
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| cosine_accuracy@5 | 1.0 | |
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| cosine_accuracy@10 | 1.0 | |
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| cosine_precision@1 | 0.9167 | |
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| cosine_precision@3 | 0.3333 | |
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| cosine_precision@5 | 0.2 | |
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| cosine_precision@10 | 0.1 | |
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| cosine_recall@1 | 0.9167 | |
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| cosine_recall@3 | 1.0 | |
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| cosine_recall@5 | 1.0 | |
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| cosine_recall@10 | 1.0 | |
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| **cosine_ndcg@10** | **0.9692** | |
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| cosine_mrr@10 | 0.9583 | |
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| cosine_map@100 | 0.9583 | |
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<!-- |
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## Bias, Risks and Limitations |
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*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
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--> |
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<!-- |
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### Recommendations |
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*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
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--> |
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## Training Details |
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### Training Dataset |
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#### Unnamed Dataset |
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* Size: 156 training samples |
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* Columns: <code>sentence_0</code> and <code>sentence_1</code> |
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* Approximate statistics based on the first 156 samples: |
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| | sentence_0 | sentence_1 | |
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|:--------|:-----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------| |
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| type | string | string | |
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| details | <ul><li>min: 12 tokens</li><li>mean: 21.09 tokens</li><li>max: 37 tokens</li></ul> | <ul><li>min: 43 tokens</li><li>mean: 135.28 tokens</li><li>max: 214 tokens</li></ul> | |
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* Samples: |
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| sentence_0 | sentence_1 | |
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|:------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
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| <code>What were some of the economic consequences of the railway construction boom in the 1800s?</code> | <code>An interesting point of comparison here could be the way railways rolled out around the world in the 1800s. Constructing these required enormous investments and had a massive environmental impact, and many of the lines that were built turned out to be unnecessary—sometimes multiple lines from different companies serving the exact same routes!<br>The resulting bubbles contributed to several financial crashes, see Wikipedia for Panic of 1873, Panic of 1893, Panic of 1901 and the UK’s Railway Mania. They left us with a lot of useful infrastructure and a great deal of bankruptcies and environmental damage.<br>The year of slop</code> | |
|
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| <code>How did the construction of railways in the 1800s impact the environment?</code> | <code>An interesting point of comparison here could be the way railways rolled out around the world in the 1800s. Constructing these required enormous investments and had a massive environmental impact, and many of the lines that were built turned out to be unnecessary—sometimes multiple lines from different companies serving the exact same routes!<br>The resulting bubbles contributed to several financial crashes, see Wikipedia for Panic of 1873, Panic of 1893, Panic of 1901 and the UK’s Railway Mania. They left us with a lot of useful infrastructure and a great deal of bankruptcies and environmental damage.<br>The year of slop</code> | |
|
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| <code>What recent development did Meta contribute to the field of large language models in December?</code> | <code>Nothing yet from Anthropic or Meta but I would be very surprised if they don’t have their own inference-scaling models in the works. Meta published a relevant paper Training Large Language Models to Reason in a Continuous Latent Space in December.<br>Was the best currently available LLM trained in China for less than $6m?<br>Not quite, but almost! It does make for a great attention-grabbing headline.<br>The big news to end the year was the release of DeepSeek v3—dropped on Hugging Face on Christmas Day without so much as a README file, then followed by documentation and a paper the day after that.</code> | |
|
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* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: |
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```json |
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{ |
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"loss": "MultipleNegativesRankingLoss", |
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"matryoshka_dims": [ |
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|
768, |
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|
512, |
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|
256, |
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|
128, |
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|
64 |
|
|
], |
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|
"matryoshka_weights": [ |
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|
1, |
|
|
1, |
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1, |
|
|
1, |
|
|
1 |
|
|
], |
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|
"n_dims_per_step": -1 |
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|
} |
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|
``` |
|
|
|
|
|
### Training Hyperparameters |
|
|
#### Non-Default Hyperparameters |
|
|
|
|
|
- `eval_strategy`: steps |
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|
- `per_device_train_batch_size`: 10 |
|
|
- `per_device_eval_batch_size`: 10 |
|
|
- `num_train_epochs`: 10 |
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|
- `multi_dataset_batch_sampler`: round_robin |
|
|
|
|
|
#### All Hyperparameters |
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|
<details><summary>Click to expand</summary> |
|
|
|
|
|
- `overwrite_output_dir`: False |
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|
- `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 |
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|
- `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`: 10 |
|
|
- `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 |
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|
- `fp16`: False |
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|
- `fp16_opt_level`: O1 |
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|
- `half_precision_backend`: auto |
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|
- `bf16_full_eval`: False |
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|
- `fp16_full_eval`: False |
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|
- `tf32`: None |
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|
- `local_rank`: 0 |
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|
- `ddp_backend`: None |
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|
- `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} |
|
|
- `tp_size`: 0 |
|
|
- `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 |
|
|
- `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 |
|
|
|
|
|
</details> |
|
|
|
|
|
### Training Logs |
|
|
| Epoch | Step | cosine_ndcg@10 | |
|
|
|:-----:|:----:|:--------------:| |
|
|
| 1.0 | 16 | 0.9401 | |
|
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| 2.0 | 32 | 0.9539 | |
|
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| 3.0 | 48 | 0.9638 | |
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| 3.125 | 50 | 0.9692 | |
|
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| 4.0 | 64 | 0.9692 | |
|
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| 5.0 | 80 | 0.9692 | |
|
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| 6.0 | 96 | 0.9692 | |
|
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| 6.25 | 100 | 0.9692 | |
|
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| 7.0 | 112 | 0.9692 | |
|
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| 8.0 | 128 | 0.9692 | |
|
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| 9.0 | 144 | 0.9692 | |
|
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| 9.375 | 150 | 0.9692 | |
|
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| 10.0 | 160 | 0.9692 | |
|
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|
|
|
|
|
|
### Framework Versions |
|
|
- Python: 3.11.12 |
|
|
- Sentence Transformers: 4.1.0 |
|
|
- Transformers: 4.51.3 |
|
|
- PyTorch: 2.6.0+cu124 |
|
|
- Accelerate: 1.6.0 |
|
|
- Datasets: 3.5.1 |
|
|
- Tokenizers: 0.21.1 |
|
|
|
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|
## Citation |
|
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|
|
|
### BibTeX |
|
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|
|
|
#### 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", |
|
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month = "11", |
|
|
year = "2019", |
|
|
publisher = "Association for Computational Linguistics", |
|
|
url = "https://arxiv.org/abs/1908.10084", |
|
|
} |
|
|
``` |
|
|
|
|
|
#### MatryoshkaLoss |
|
|
```bibtex |
|
|
@misc{kusupati2024matryoshka, |
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title={Matryoshka Representation Learning}, |
|
|
author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi}, |
|
|
year={2024}, |
|
|
eprint={2205.13147}, |
|
|
archivePrefix={arXiv}, |
|
|
primaryClass={cs.LG} |
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} |
|
|
``` |
|
|
|
|
|
#### MultipleNegativesRankingLoss |
|
|
```bibtex |
|
|
@misc{henderson2017efficient, |
|
|
title={Efficient Natural Language Response Suggestion for Smart Reply}, |
|
|
author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, |
|
|
year={2017}, |
|
|
eprint={1705.00652}, |
|
|
archivePrefix={arXiv}, |
|
|
primaryClass={cs.CL} |
|
|
} |
|
|
``` |
|
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