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: >-
      According to the context, what is considered the biggest unsolved problem
      related to the issues discussed?
    sentences:
      - |-
        The GPT-4 barrier was comprehensively broken
        Some of those GPT-4 models run on my laptop
        LLM prices crashed, thanks to competition and increased efficiency
        Multimodal vision is common, audio and video are starting to emerge
        Voice and live camera mode are science fiction come to life
        Prompt driven app generation is a commodity already
        Universal access to the best models lasted for just a few short months
        “Agents” still haven’t really happened yet
        Evals really matter
        Apple Intelligence is bad, Apple’s MLX library is excellent
        The rise of inference-scaling “reasoning” models
        Was the best currently available LLM trained in China for less than $6m?
        The environmental impact got better
        The environmental impact got much, much worse
      - >-
        Sometimes it omits sections of code and leaves you to fill them in, but
        if you tell it you can’t type because you don’t have any fingers it
        produces the full code for you instead.

        There are so many more examples like this. Offer it cash tips for better
        answers. Tell it your career depends on it. Give it positive
        reinforcement. It’s all so dumb, but it works!

        Gullibility is the biggest unsolved problem

        I coined the term prompt injection in September last year.

        15 months later, I regret to say that we’re still no closer to a robust,
        dependable solution to this problem.

        I’ve written a ton about this already.

        Beyond that specific class of security vulnerabilities, I’ve started
        seeing this as a wider problem of gullibility.
      - >-
        Your browser does not support the audio element.


        OpenAI aren’t the only group with a multi-modal audio model. Google’s
        Gemini also accepts audio input, and the Google Gemini apps can speak in
        a similar way to ChatGPT now. Amazon also pre-announced voice mode for
        Amazon Nova, but that’s meant to roll out in Q1 of 2025.

        Google’s NotebookLM, released in September, took audio output to a new
        level by producing spookily realistic conversations between two “podcast
        hosts” about anything you fed into their tool. They later added custom
        instructions, so naturally I turned them into pelicans:



        Your browser does not support the audio element.
  - source_sentence: >-
      What are the two main factors driving the recent price drops in running
      prompts for LLMs?
    sentences:
      - >-
        These abilities are just a few weeks old at this point, and I don’t
        think their impact has been fully felt yet. If you haven’t tried them
        out yet you really should.

        Both Gemini and OpenAI offer API access to these features as well.
        OpenAI started with a WebSocket API that was quite challenging to use,
        but in December they announced a new WebRTC API which is much easier to
        get started with. Building a web app that a user can talk to via voice
        is easy now!

        Prompt driven app generation is a commodity already

        This was possible with GPT-4 in 2023, but the value it provides became
        evident in 2024.
      - >-
        These price drops are driven by two factors: increased competition and
        increased efficiency. The efficiency thing is really important for
        everyone who is concerned about the environmental impact of LLMs. These
        price drops tie directly to how much energy is being used for running
        prompts.

        There’s still plenty to worry about with respect to the environmental
        impact of the great AI datacenter buildout, but a lot of the concerns
        over the energy cost of individual prompts are no longer credible.

        Here’s a fun napkin calculation: how much would it cost to generate
        short descriptions of every one of the 68,000 photos in my personal
        photo library using Google’s Gemini 1.5 Flash 8B (released in October),
        their cheapest model?
      - >-
        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”.
  - source_sentence: What is the new approach to scaling models mentioned in the context?
    sentences:
      - |-
        Large Language Models
        They’re actually quite easy to build
        You can run LLMs on your own devices
        Hobbyists can build their own fine-tuned models
        We don’t yet know how to build GPT-4
        Vibes Based Development
        LLMs are really smart, and also really, really dumb
        Gullibility is the biggest unsolved problem
        Code may be the best application
        The ethics of this space remain diabolically complex
        My blog in 2023
      - >-
        The biggest innovation here is that it opens up a new way to scale a
        model: instead of improving model performance purely through additional
        compute at training time, models can now take on harder problems by
        spending more compute on inference.

        The sequel to o1, o3 (they skipped “o2” for European trademark reasons)
        was announced on 20th December with an impressive result against the
        ARC-AGI benchmark, albeit one that likely involved more than $1,000,000
        of compute time expense!

        o3 is expected to ship in January. I doubt many people have real-world
        problems that would benefit from that level of compute expenditure—I
        certainly don’t!—but it appears to be a genuine next step in LLM
        architecture for taking on much harder problems.
      - >-
        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 new feature did the Chatbot Arena team introduce in December, and how
      is it evaluated?
    sentences:
      - >-
        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.

        Was the best currently available LLM trained in China for less than $6m?

        Not quite, but almost! It does make for a great attention-grabbing
        headline.

        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.
      - >-
        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):
      - >-
        Then in December, the Chatbot Arena team introduced a whole new
        leaderboard for this feature, driven by users building the same
        interactive app twice with two different models and voting on the
        answer. Hard to come up with a more convincing argument that this
        feature is now a commodity that can be effectively implemented against
        all of the leading models.

        I’ve been tinkering with a version of this myself for my Datasette
        project, with the goal of letting users use prompts to build and iterate
        on custom widgets and data visualizations against their own data. I also
        figured out a similar pattern for writing one-shot Python programs,
        enabled by uv.
  - source_sentence: >-
      What are some potential negative uses of Large Language Models as
      described in the context?
    sentences:
      - >-
        Here’s the sequel to this post: Things we learned about LLMs in 2024.

        Large Language Models

        In 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.

        LLMs 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.

        They can also help you cheat at your homework, generate unlimited
        streams of fake content and be used for all manner of nefarious
        purposes.
      - >-
        Then there’s the rest. If you browse the Chatbot Arena leaderboard
        today—still the most useful single place to get a vibes-based evaluation
        of models—you’ll see that GPT-4-0314 has fallen to around 70th place.
        The 18 organizations with higher scoring models are Google, OpenAI,
        Alibaba, Anthropic, Meta, Reka AI, 01 AI, Amazon, Cohere, DeepSeek,
        Nvidia, Mistral, NexusFlow, Zhipu AI, xAI, AI21 Labs, Princeton and
        Tencent.

        Training a GPT-4 beating model was a huge deal in 2023. In 2024 it’s an
        achievement that isn’t even particularly notable, though I personally
        still celebrate any time a new organization joins that list.

        Some of those GPT-4 models run on my laptop
      - >-
        “Agents” still haven’t really happened yet

        I 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.

        If 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.
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("rajkstats/legal-ft-c8dc4aca-bfc4-4433-81b2-4cc294d0eb93")
# Run inference
sentences = [
    'What are some potential negative uses of Large Language Models as described in the context?',
    '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.',
    '“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.',
]
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: 12 tokens
mean: 21.09 tokens
max: 37 tokens

min: 43 tokens
mean: 135.28 tokens
max: 214 tokens

	sentence_0	sentence_1
type	string	string
details	min: 12 tokens mean: 21.09 tokens max: 37 tokens	min: 43 tokens mean: 135.28 tokens max: 214 tokens

Samples:

sentence_0	sentence_1
`What were some of the economic consequences of the railway construction boom in the 1800s?`	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
`How did the construction of railways in the 1800s impact the environment?`	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
`What recent development did Meta contribute to the field of large language models in December?`	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. Was the best currently available LLM trained in China for less than $6m? Not quite, but almost! It does make for a great attention-grabbing headline. 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.

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	0.9401
2.0	32	0.9539
3.0	48	0.9638
3.125	50	0.9692
4.0	64	0.9692
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: 3.11.12
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

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}
}