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 significant change occurred in the AI landscape regarding models
      surpassing GPT-4 in the past twelve months?
    sentences:
      - >-
        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?
      - >-
        The GPT-4 barrier was comprehensively broken

        In my December 2023 review I wrote about how We don’t yet know how to
        build GPT-4—OpenAI’s best model was almost a year old at that point, yet
        no other AI lab had produced anything better. What did OpenAI know that
        the rest of us didn’t?

        I’m relieved that this has changed completely in the past twelve months.
        18 organizations now have models on the Chatbot Arena Leaderboard that
        rank higher than the original GPT-4 from March 2023 (GPT-4-0314 on the
        board)—70 models in total.
      - >-
        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.
  - source_sentence: >-
      How does Claude enable users to interact with applications created through
      its interface?
    sentences:
      - >-
        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.
      - >-
        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:
      - >-
        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 or Twitter
        or subscribe to my newsletter



        More recent articles


        LLM 0.22, the annotated release notes - 17th February 2025

        Run LLMs on macOS using llm-mlx and Apple's MLX framework - 15th
        February 2025

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        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
                    69
  - source_sentence: >-
      What incident involving Google Search is mentioned in the context, and
      what was the nature of the misinformation?
    sentences:
      - >-
        My personal laptop is a 64GB M2 MacBook Pro from 2023. It’s a powerful
        machine, but it’s also nearly two years old now—and crucially it’s the
        same laptop I’ve been using ever since I first ran an LLM on my computer
        back in March 2023 (see Large language models are having their Stable
        Diffusion moment).

        That same laptop that could just about run a GPT-3-class model in March
        last year has now run multiple GPT-4 class models! Some of my notes on
        that:
      - >-
        Terminology aside, I remain skeptical as to their utility based, once
        again, on the challenge of gullibility. LLMs believe anything you tell
        them. Any systems that attempts to make meaningful decisions on your
        behalf will run into the same roadblock: how good is a travel agent, or
        a digital assistant, or even a research tool if it can’t distinguish
        truth from fiction?

        Just the other day Google Search was caught serving up an entirely fake
        description of the non-existant movie “Encanto 2”. It turned out to be
        summarizing an imagined movie listing from a fan fiction wiki.
      - >-
        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: >-
      What are the limitations of Apple's LLM features compared to frontier
      LLMs, according to the context?
    sentences:
      - >-
        DeepSeek v3 is a huge 685B parameter model—one of the largest openly
        licensed models currently available, significantly bigger than the
        largest of Meta’s Llama series, Llama 3.1 405B.

        Benchmarks put it up there with Claude 3.5 Sonnet. Vibe benchmarks (aka
        the Chatbot Arena) currently rank it 7th, just behind the Gemini 2.0 and
        OpenAI 4o/o1 models. This is by far the highest ranking openly licensed
        model.

        The really impressive thing about DeepSeek v3 is the training cost. The
        model was trained on 2,788,000 H800 GPU hours at an estimated cost of
        $5,576,000. Llama 3.1 405B trained 30,840,000 GPU hours—11x that used by
        DeepSeek v3, for a model that benchmarks slightly worse.
      - >-
        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
      - >-
        Now that those features are rolling out they’re pretty weak. As an LLM
        power-user I know what these models are capable of, and Apple’s LLM
        features offer a pale imitation of what a frontier LLM can do. Instead
        we’re getting notification summaries that misrepresent news headlines
        and writing assistant tools that I’ve not found useful at all. Genmoji
        are kind of fun though.

        The rise of inference-scaling “reasoning” models

        The most interesting development in the final quarter of 2024 was the
        introduction of a new shape of LLM, exemplified by OpenAI’s o1
        models—initially released as o1-preview and o1-mini on September 12th.
  - source_sentence: What new feature was introduced in ChatGPT's voice mode in December?
    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.
      - >-
        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”.
      - >-
        The GPT-4 barrier was comprehensively broken

        In my December 2023 review I wrote about how We don’t yet know how to
        build GPT-4—OpenAI’s best model was almost a year old at that point, yet
        no other AI lab had produced anything better. What did OpenAI know that
        the rest of us didn’t?

        I’m relieved that this has changed completely in the past twelve months.
        18 organizations now have models on the Chatbot Arena Leaderboard that
        rank higher than the original GPT-4 from March 2023 (GPT-4-0314 on the
        board)—70 models in total.
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
          - 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: 0.9166666666666666
            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: 0.9166666666666666
            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: 0.9692441461309548
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.9583333333333334
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.9583333333333334
            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("dabraldeepti25/legal-ft-v0-updated")
# Run inference
sentences = [
    "What new feature was introduced in ChatGPT's voice mode in December?",
    '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.',
    'The GPT-4 barrier was comprehensively broken\nIn my December 2023 review I wrote about how We don’t yet know how to build GPT-4—OpenAI’s best model was almost a year old at that point, yet no other AI lab had produced anything better. What did OpenAI know that the rest of us didn’t?\nI’m relieved that this has changed completely in the past twelve months. 18 organizations now have models on the Chatbot Arena Leaderboard that rank higher than the original GPT-4 from March 2023 (GPT-4-0314 on the board)—70 models in total.',
]
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	0.9167
cosine_accuracy@3	1.0
cosine_accuracy@5	1.0
cosine_accuracy@10	1.0
cosine_precision@1	0.9167
cosine_precision@3	0.3333
cosine_precision@5	0.2
cosine_precision@10	0.1
cosine_recall@1	0.9167
cosine_recall@3	1.0
cosine_recall@5	1.0
cosine_recall@10	1.0
cosine_ndcg@10	0.9692
cosine_mrr@10	0.9583
cosine_map@100	0.9583

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: 13 tokens
mean: 20.17 tokens
max: 34 tokens

min: 43 tokens
mean: 135.18 tokens
max: 214 tokens

	sentence_0	sentence_1
type	string	string
details	min: 13 tokens mean: 20.17 tokens max: 34 tokens	min: 43 tokens mean: 135.18 tokens max: 214 tokens

Samples:

sentence_0	sentence_1
`What is the significance of prompt engineering in DALL-E 3?`	Now add a walrus: Prompt engineering in DALL-E 3 32.8k 41.2k Web LLM runs the vicuna-7b Large Language Model entirely in your browser, and it’s very impressive 32.5k 38.2k ChatGPT can’t access the internet, even though it really looks like it can 30.5k 34.2k Stanford Alpaca, and the acceleration of on-device large language model development 29.7k 35.7k Run Llama 2 on your own Mac using LLM and Homebrew 27.9k 33.6k Midjourney 5.1 26.7k 33.4k Think of language models like ChatGPT as a “calculator for words” 25k 31.8k Multi-modal prompt injection image attacks against GPT-4V 23.7k 27.4k
`How does the vicuna-7b Large Language Model operate within a browser?`	Now add a walrus: Prompt engineering in DALL-E 3 32.8k 41.2k Web LLM runs the vicuna-7b Large Language Model entirely in your browser, and it’s very impressive 32.5k 38.2k ChatGPT can’t access the internet, even though it really looks like it can 30.5k 34.2k Stanford Alpaca, and the acceleration of on-device large language model development 29.7k 35.7k Run Llama 2 on your own Mac using LLM and Homebrew 27.9k 33.6k Midjourney 5.1 26.7k 33.4k Think of language models like ChatGPT as a “calculator for words” 25k 31.8k Multi-modal prompt injection image attacks against GPT-4V 23.7k 27.4k
`What model of MacBook Pro is being used in the context, and what is its storage capacity?`	`My personal laptop is a 64GB M2 MacBook Pro from 2023. It’s a powerful machine, but it’s also nearly two years old now—and crucially it’s the same laptop I’ve been using ever since I first ran an LLM on my computer back in March 2023 (see Large language models are having their Stable Diffusion moment). That same laptop that could just about run a GPT-3-class model in March last year has now run multiple GPT-4 class models! Some of my notes on that:`

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}
fsdp_transformer_layer_cls_to_wrap: None
accelerator_config: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
deepspeed: None
label_smoothing_factor: 0.0
optim: adamw_torch
optim_args: None
adafactor: False
group_by_length: False
length_column_name: length
ddp_find_unused_parameters: None
ddp_bucket_cap_mb: None
ddp_broadcast_buffers: False
dataloader_pin_memory: True
dataloader_persistent_workers: False
skip_memory_metrics: True
use_legacy_prediction_loop: False
push_to_hub: False
resume_from_checkpoint: None
hub_model_id: None
hub_strategy: every_save
hub_private_repo: None
hub_always_push: False
gradient_checkpointing: False
gradient_checkpointing_kwargs: None
include_inputs_for_metrics: False
include_for_metrics: []
eval_do_concat_batches: True
fp16_backend: auto
push_to_hub_model_id: None
push_to_hub_organization: None
mp_parameters:
auto_find_batch_size: False
full_determinism: False
torchdynamo: None
ray_scope: last
ddp_timeout: 1800
torch_compile: False
torch_compile_backend: None
torch_compile_mode: None
dispatch_batches: None
split_batches: None
include_tokens_per_second: False
include_num_input_tokens_seen: False
neftune_noise_alpha: None
optim_target_modules: None
batch_eval_metrics: False
eval_on_start: False
use_liger_kernel: False
eval_use_gather_object: False
average_tokens_across_devices: False
prompts: None
batch_sampler: batch_sampler
multi_dataset_batch_sampler: round_robin

Training Logs

Epoch	Step	cosine_ndcg@10
1.0	16	0.9692
2.0	32	0.9692
3.0	48	1.0
3.125	50	1.0
4.0	64	1.0
5.0	80	0.9692
6.0	96	0.9692
6.25	100	0.9692
7.0	112	0.9692
8.0	128	0.9692
9.0	144	0.9692
9.375	150	0.9692
10.0	160	0.9692

Framework Versions

Python: 3.11.11
Sentence Transformers: 3.4.1
Transformers: 4.48.3
PyTorch: 2.5.1+cu124
Accelerate: 1.3.0
Datasets: 3.3.1
Tokenizers: 0.21.0

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}
}