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: >-
      How did Steve Krouse from Val Town demonstrate the capabilities of a 2,000
      token/second LLM?
    sentences:
      - >-
        The most recent twist, again from December (December was a lot) is live
        video. ChatGPT voice mode now provides the option to share your camera
        feed with the model and talk about what you can see in real time. Google
        Gemini have a preview of the same feature, which they managed to ship
        the day before ChatGPT did.
      - >-
        I’ve found myself using this a lot. I noticed how much I was relying on
        it in October and wrote Everything I built with Claude Artifacts this
        week, describing 14 little tools I had put together in a seven day
        period.

        Since then, a whole bunch of other teams have built similar systems.
        GitHub announced their version of this—GitHub Spark—in October. Mistral
        Chat added it as a feature called Canvas in November.

        Steve Krouse from Val Town built a version of it against Cerebras,
        showcasing how a 2,000 token/second LLM can iterate on an application
        with changes visible in less than a second.
      - >-
        I run a bunch of them on my laptop. I run Mistral 7B (a surprisingly
        great model) on my iPhone. You can install several different apps to get
        your own, local, completely private LLM. My own LLM project provides a
        CLI tool for running an array of different models via plugins.

        You can even run them entirely in your browser using WebAssembly and the
        latest Chrome!

        Hobbyists can build their own fine-tuned models

        I said earlier that building an LLM was still out of reach of hobbyists.
        That may be true for training from scratch, but fine-tuning one of those
        models is another matter entirely.
  - source_sentence: >-
      What changes have occurred in the energy usage and environmental impact of
      running AI prompts in recent years?
    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):
      - >-
        Those US export regulations on GPUs to China seem to have inspired some
        very effective training optimizations!

        The environmental impact got better

        A welcome result of the increased efficiency of the models—both the
        hosted ones and the ones I can run locally—is that the energy usage and
        environmental impact of running a prompt has dropped enormously over the
        past couple of years.

        OpenAI themselves are charging 100x less for a prompt compared to the
        GPT-3 days. I have it on good authority that neither Google Gemini nor
        Amazon Nova (two of the least expensive model providers) are running
        prompts at a loss.
      - >-
        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!

        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.

        The year of slop
  - source_sentence: >-
      What is the main topic discussed in the article titled "Industry’s Tardy
      Response to the AI Prompt Injection Vulnerability" on RedMonk
      Conversations?
    sentences:
      - >-
        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).
      - >-
        Industry’s Tardy Response to the AI Prompt Injection Vulnerability on
        RedMonk Conversations



        Posted 31st December 2023 at 11:59 pm · Follow me on Mastodon, Bluesky,
        Twitter or subscribe to my newsletter



        More recent articles


        Live blog: Claude 4 launch at Code with Claude - 22nd May 2025

        I really don't like ChatGPT's new memory dossier - 21st May 2025

        Building software on top of Large Language Models - 15th May 2025


         


        This is Stuff we figured out about AI in 2023 by Simon Willison, posted
        on 31st December 2023.


        Part of series LLMs annual review


        Stuff we figured out about AI in 2023 - Dec. 31, 2023, 11:59 p.m. 

        Things we learned about LLMs in 2024 - Dec. 31, 2024, 6:07 p.m. 



                    blogging
                    105
      - >-
        When ChatGPT Advanced Voice mode finally did roll out (a slow roll from
        August through September) it was spectacular. I’ve been using it
        extensively on walks with my dog and it’s amazing how much the
        improvement in intonation elevates the material. I’ve also had a lot of
        fun experimenting with the OpenAI audio APIs.

        Even more fun: Advanced Voice mode can do accents! Here’s what happened
        when I told it I need you to pretend to be a California brown pelican
        with a very thick Russian accent, but you talk to me exclusively in
        Spanish.
  - source_sentence: >-
      How can LLMs like Claude create full interactive applications using web
      technologies in a single prompt?
    sentences:
      - >-
        This prompt-driven custom interface feature is so powerful and easy to
        build (once you’ve figured out the gnarly details of browser sandboxing)
        that I expect it to show up as a feature in a wide range of products in
        2025.

        Universal access to the best models lasted for just a few short months

        For a few short months this year all three of the best available
        models—GPT-4o, Claude 3.5 Sonnet and Gemini 1.5 Pro—were freely
        available to most of the world.
      - >-
        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
      - >-
        We already knew LLMs were spookily good at writing code. If you prompt
        them right, it turns out they can build you a full interactive
        application using HTML, CSS and JavaScript (and tools like React if you
        wire up some extra supporting build mechanisms)—often in a single
        prompt.

        Anthropic kicked this idea into high gear when they released Claude
        Artifacts, a groundbreaking new feature that was initially slightly lost
        in the noise due to being described half way through their announcement
        of the incredible Claude 3.5 Sonnet.

        With Artifacts, Claude can write you an on-demand interactive
        application and then let you use it directly inside the Claude
        interface.

        Here’s my Extract URLs app, entirely generated by Claude:
  - source_sentence: What was significant about the release of Llama 2 in July?
    sentences:
      - >-
        Then in February, Meta released Llama. And a few weeks later in March,
        Georgi Gerganov released code that got it working on a MacBook.

        I wrote about how Large language models are having their Stable
        Diffusion moment, and with hindsight that was a very good call!

        This unleashed a whirlwind of innovation, which was accelerated further
        in July when Meta released Llama 2—an improved version which, crucially,
        included permission for commercial use.

        Today there are literally thousands of LLMs that can be run locally, on
        all manner of different devices.
      - >-
        OpenAI made GPT-4o free for all users in May, and Claude 3.5 Sonnet was
        freely available from its launch in June. This was a momentus change,
        because for the previous year free users had mostly been restricted to
        GPT-3.5 level models, meaning new users got a very inaccurate mental
        model of what a capable LLM could actually do.

        That era appears to have ended, likely permanently, with OpenAI’s launch
        of ChatGPT Pro. This $200/month subscription service is the only way to
        access their most capable model, o1 Pro.

        Since the trick behind the o1 series (and the future models it will
        undoubtedly inspire) is to expend more compute time to get better
        results, I don’t think those days of free access to the best available
        models are likely to return.
      - >-
        Prompt injection is a natural consequence of this gulibility. I’ve seen
        precious little progress on tackling that problem in 2024, and we’ve
        been talking about it since September 2022.

        I’m beginning to see the most popular idea of “agents” as dependent on
        AGI itself. A model that’s robust against gulliblity is a very tall
        order indeed.

        Evals really matter

        Anthropic’s Amanda Askell (responsible for much of the work behind
        Claude’s Character):
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: 0.8333333333333334
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 1
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 1
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 1
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.8333333333333334
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.3333333333333333
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.20000000000000004
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.10000000000000002
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.8333333333333334
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 1
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 1
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 1
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.9384882922619097
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.9166666666666666
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.9166666666666666
            name: Cosine Map@100

SentenceTransformer based on Snowflake/snowflake-arctic-embed-l

This is a sentence-transformers model finetuned from 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
Maximum Sequence Length: 512 tokens
Output Dimensionality: 1024 dimensions
Similarity Function: Cosine Similarity

Model Sources

Documentation: Sentence Transformers Documentation
Repository: Sentence Transformers on GitHub
Hugging Face: Sentence Transformers on Hugging Face

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 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:

pip install -U sentence-transformers

Then you can load this model and run inference.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("dwb2023/legal-ft-b5869012-93ce-4e45-bca9-2eb86f3ef4b9")
# Run inference
sentences = [
    'What was significant about the release of Llama 2 in July?',
    'Then in February, Meta released Llama. And a few weeks later in March, Georgi Gerganov released code that got it working on a MacBook.\nI wrote about how Large language models are having their Stable Diffusion moment, and with hindsight that was a very good call!\nThis unleashed a whirlwind of innovation, which was accelerated further in July when Meta released Llama 2—an improved version which, crucially, included permission for commercial use.\nToday there are literally thousands of LLMs that can be run locally, on all manner of different devices.',
    'Prompt injection is a natural consequence of this gulibility. I’ve seen precious little progress on tackling that problem in 2024, and we’ve been talking about it since September 2022.\nI’m beginning to see the most popular idea of “agents” as dependent on AGI itself. A model that’s robust against gulliblity is a very tall order indeed.\nEvals really matter\nAnthropic’s Amanda Askell (responsible for much of the work behind Claude’s Character):',
]
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

Metric	Value
cosine_accuracy@1	0.8333
cosine_accuracy@3	1.0
cosine_accuracy@5	1.0
cosine_accuracy@10	1.0
cosine_precision@1	0.8333
cosine_precision@3	0.3333
cosine_precision@5	0.2
cosine_precision@10	0.1
cosine_recall@1	0.8333
cosine_recall@3	1.0
cosine_recall@5	1.0
cosine_recall@10	1.0
cosine_ndcg@10	0.9385
cosine_mrr@10	0.9167
cosine_map@100	0.9167

Training Details

Training Dataset

Unnamed Dataset

Size: 156 training samples
Columns: sentence_0 and sentence_1
Approximate statistics based on the first 156 samples:
sentence_0 sentence_1
type string string
details
min: 12 tokens
mean: 20.89 tokens
max: 32 tokens

min: 43 tokens
mean: 135.1 tokens
max: 214 tokens

	sentence_0	sentence_1
type	string	string
details	min: 12 tokens mean: 20.89 tokens max: 32 tokens	min: 43 tokens mean: 135.1 tokens max: 214 tokens

Samples:

sentence_0	sentence_1
`What are some of the topics covered in the annotated presentations given in 2023?`	I also gave a bunch of talks and podcast appearances. I’ve started habitually turning my talks into annotated presentations—here are my best from 2023: Prompt injection explained, with video, slides, and a transcript Catching up on the weird world of LLMs Making Large Language Models work for you Open questions for AI engineering Embeddings: What they are and why they matter Financial sustainability for open source projects at GitHub Universe And in podcasts: What AI can do for you on the Theory of Change Working in public on Path to Citus Con LLMs break the internet on the Changelog Talking Large Language Models on Rooftop Ruby Thoughts on the OpenAI board situation on Newsroom Robots
`Which podcasts featured discussions related to Large Language Models and AI topics?`	I also gave a bunch of talks and podcast appearances. I’ve started habitually turning my talks into annotated presentations—here are my best from 2023: Prompt injection explained, with video, slides, and a transcript Catching up on the weird world of LLMs Making Large Language Models work for you Open questions for AI engineering Embeddings: What they are and why they matter Financial sustainability for open source projects at GitHub Universe And in podcasts: What AI can do for you on the Theory of Change Working in public on Path to Citus Con LLMs break the internet on the Changelog Talking Large Language Models on Rooftop Ruby Thoughts on the OpenAI board situation on Newsroom Robots
`What is the main subject of the New York Times' lawsuit against OpenAI and Microsoft?`	Just this week, the New York Times launched a landmark lawsuit against OpenAI and Microsoft over this issue. The 69 page PDF is genuinely worth reading—especially the first few pages, which lay out the issues in a way that’s surprisingly easy to follow. The rest of the document includes some of the clearest explanations of what LLMs are, how they work and how they are built that I’ve read anywhere. The legal arguments here are complex. I’m not a lawyer, but I don’t think this one will be easily decided. Whichever way it goes, I expect this case to have a profound impact on how this technology develops in the future.

Loss: MatryoshkaLoss with these parameters:

{
    "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: 10
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: 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
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}
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

Training Logs

Epoch	Step	cosine_ndcg@10
1.0	16	0.9330
2.0	32	0.9539
3.0	48	0.9484
3.125	50	0.9484
4.0	64	0.9385
5.0	80	0.9539
6.0	96	0.9539
6.25	100	0.9539
7.0	112	0.9385
8.0	128	0.9385
9.0	144	0.9385
9.375	150	0.9385
10.0	160	0.9385

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: 3.6.0
Tokenizers: 0.21.1

Citation

BibTeX

Sentence Transformers

@inproceedings{reimers-2019-sentence-bert,
    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
    author = "Reimers, Nils and Gurevych, Iryna",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
    month = "11",
    year = "2019",
    publisher = "Association for Computational Linguistics",
    url = "https://arxiv.org/abs/1908.10084",
}

MatryoshkaLoss

@misc{kusupati2024matryoshka,
    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}
}

MultipleNegativesRankingLoss

@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}
}