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, 'architecture': '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.

python

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("rajatgupta99924/AIE6-S09-eca4bfc6-eb64-44a4-a71d-e09bf2b78f50")
# Run inference
queries = [
    "What type of dish is shown in the photo and what does it contain?",
]
documents = [
    'Against this photo of butterflies at the California Academy of Sciences:\n\n\nA shallow dish, likely a hummingbird or butterfly feeder, is red.  Pieces of orange slices of fruit are visible inside the dish.\nTwo butterflies are positioned in the feeder, one is a dark brown/black butterfly with white/cream-colored markings.  The other is a large, brown butterfly with patterns of lighter brown, beige, and black markings, including prominent eye spots. The larger brown butterfly appears to be feeding on the fruit.',
    'Except... you can run generated code to see if it’s correct. And with patterns like ChatGPT Code Interpreter the LLM can execute the code itself, process the error message, then rewrite it and keep trying until it works!\nSo 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!\nHow should we feel about this as software engineers?\nOn the one hand, this feels like a threat: who needs a programmer if ChatGPT can write code for you?',
    'On the other hand, as software engineers we are better placed to take advantage of this than anyone else. We’ve all been given weird coding interns—we can use our deep knowledge to prompt them to solve coding problems more effectively than anyone else can.\nThe ethics of this space remain diabolically complex\nIn September last year Andy Baio and I produced the first major story on the unlicensed training data behind Stable Diffusion.\nSince then, almost every major LLM (and most of the image generation models) have also been trained on unlicensed data.',
]
query_embeddings = model.encode_query(queries)
document_embeddings = model.encode_document(documents)
print(query_embeddings.shape, document_embeddings.shape)
# [1, 1024] [3, 1024]

# Get the similarity scores for the embeddings
similarities = model.similarity(query_embeddings, document_embeddings)
print(similarities)
# tensor([[ 0.4179, -0.0420,  0.0399]])

Evaluation

Metrics

Information Retrieval

Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.95
cosine_accuracy@3	1.0
cosine_accuracy@5	1.0
cosine_accuracy@10	1.0
cosine_precision@1	0.95
cosine_precision@3	0.3333
cosine_precision@5	0.2
cosine_precision@10	0.1
cosine_recall@1	0.95
cosine_recall@3	1.0
cosine_recall@5	1.0
cosine_recall@10	1.0
cosine_ndcg@10	0.9815
cosine_mrr@10	0.975
cosine_map@100	0.975

Training Details

Training Dataset

Unnamed Dataset

Size: 160 training samples
Columns: sentence_0 and sentence_1
Approximate statistics based on the first 160 samples:
sentence_0 sentence_1
type string string
details
min: 12 tokens
mean: 20.58 tokens
max: 33 tokens
min: 34 tokens
mean: 133.43 tokens
max: 214 tokens

Samples:

sentence_0	sentence_1
`What topics are covered in the articles related to large language models (LLMs) and AI development in the provided context?`	Embeddings: What they are and why they matter[object Object]61.7k[object Object]79.3k[object Object][object Object][object Object]Catching up on the weird world of LLMs[object Object]61.6k[object Object]85.9k[object Object][object Object][object Object]llamafile is the new best way to run an LLM on your own computer[object Object]52k[object Object]66k[object Object][object Object][object Object]Prompt injection explained, with video, slides, and a transcript[object Object]51k[object Object]61.9k[object Object][object Object][object Object]AI-enhanced development makes me more ambitious with my projects[object Object]49.6k[object Object]60.1k[object Object][object Object][object Object]Understanding GPT tokenizers[object Object]49.5k[object Object]61.1k[object Object][object Object][object Object]Exploring GPTs: ChatGPT in a trench coat?[object Object]46.4k[object Object]58.5k[object Object][object Object][object Object]Could you train a ChatGPT-beating model for $85,000 and run it in a browser?[object Object]40.5k[object Object]49.2k[object Object][object Object][object Object]How to implement Q&A against your documentation with GPT3, embeddings and Datasette[object Object]37.3k[object Object]44.9k[object Object][object Object][object Object]Lawyer cites fake cases invented by ChatGPT, judge is not amused[object Object]37.1k[object Object]47.4k
`Which article discusses the potential cost and feasibility of training a ChatGPT-beating model to run in a browser?`	Embeddings: What they are and why they matter[object Object]61.7k[object Object]79.3k[object Object][object Object][object Object]Catching up on the weird world of LLMs[object Object]61.6k[object Object]85.9k[object Object][object Object][object Object]llamafile is the new best way to run an LLM on your own computer[object Object]52k[object Object]66k[object Object][object Object][object Object]Prompt injection explained, with video, slides, and a transcript[object Object]51k[object Object]61.9k[object Object][object Object][object Object]AI-enhanced development makes me more ambitious with my projects[object Object]49.6k[object Object]60.1k[object Object][object Object][object Object]Understanding GPT tokenizers[object Object]49.5k[object Object]61.1k[object Object][object Object][object Object]Exploring GPTs: ChatGPT in a trench coat?[object Object]46.4k[object Object]58.5k[object Object][object Object][object Object]Could you train a ChatGPT-beating model for $85,000 and run it in a browser?[object Object]40.5k[object Object]49.2k[object Object][object Object][object Object]How to implement Q&A against your documentation with GPT3, embeddings and Datasette[object Object]37.3k[object Object]44.9k[object Object][object Object][object Object]Lawyer cites fake cases invented by ChatGPT, judge is not amused[object Object]37.1k[object Object]47.4k
`What are some of the capabilities of Large Language Models mentioned in the context?`	Here’s the sequel to this post: Things we learned about LLMs in 2024.[object Object]Large Language Models[object Object]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.[object Object]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.[object Object]They can also help you cheat at your homework, generate unlimited streams of fake content and be used for all manner of nefarious purposes.

Loss: MatryoshkaLoss with these parameters:
json
1{ 2 "loss": "MultipleNegativesRankingLoss", 3 "matryoshka_dims": [ 4 768, 5 512, 6 256, 7 128, 8 64 9 ], 10 "matryoshka_weights": [ 11 1, 12 1, 13 1, 14 1, 15 1 16 ], 17 "n_dims_per_step": -1 18}

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}
parallelism_config: None
deepspeed: None
label_smoothing_factor: 0.0
optim: adamw_torch_fused
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
hub_revision: None
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
liger_kernel_config: None
eval_use_gather_object: False
average_tokens_across_devices: False
prompts: None
batch_sampler: batch_sampler
multi_dataset_batch_sampler: round_robin
router_mapping: {}
learning_rate_mapping: {}

Training Logs

Epoch	Step	cosine_ndcg@10
1.0	16	0.9815
2.0	32	0.9815
1.0	16	0.9815
2.0	32	0.9815
3.0	48	0.9815
3.125	50	0.9815
4.0	64	0.9815
5.0	80	0.9815
6.0	96	0.9815
6.25	100	0.9815
7.0	112	0.9815
8.0	128	0.9815
9.0	144	0.9815
9.375	150	0.9815
10.0	160	0.9815

Framework Versions

Python: 3.13.7
Sentence Transformers: 5.1.0
Transformers: 4.56.1
PyTorch: 2.8.0+cpu
Accelerate: 1.10.1
Datasets: 4.0.0
Tokenizers: 0.22.0

Citation

BibTeX

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",
    month = "11",
    year = "2019",
    publisher = "Association for Computational Linguistics",
    url = "https://arxiv.org/abs/1908.10084",
}

MatryoshkaLoss

bibtex

@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

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}
}

	sentence_0	sentence_1
type	string	string
details	min: 12 tokens mean: 20.58 tokens max: 33 tokens	min: 34 tokens mean: 133.43 tokens max: 214 tokens