This is a sentence-transformers model finetuned from sentence-transformers/all-MiniLM-L6-v2. It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for retrieval.

Model Details

Model Description

Model Type: Sentence Transformer
Base model: sentence-transformers/all-MiniLM-L6-v2
Maximum Sequence Length: 256 tokens
Output Dimensionality: 384 dimensions
Similarity Function: Cosine Similarity
Supported Modality: Text

Model Sources

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

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'transformer_task': 'feature-extraction', 'modality_config': {'text': {'method': 'forward', 'method_output_name': 'last_hidden_state'}}, 'module_output_name': 'token_embeddings', 'architecture': 'BertModel'})
  (1): Pooling({'embedding_dimension': 384, 'pooling_mode': 'mean', '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("ronit01/golden_rag_tuned_minilm")
# Run inference
sentences = [
    'How do you configure and launch a multi-config RAG evaluation experiment using run_evals(), including defining all required user-provided functions?',
    'Other Eval Config Knobs\n------\n\nFinally, apart from the Generator, the following knobs can also be included in your eval config dictionary. Each of \nthese can also be a knob set generator, viz., :func:`List()` for a discrete and :func:`Range()` for continuous knobs.\n\nFor more details on the four user-given functions listed below, see :doc:`the API: User-Provided Functions for Run Evals page</evalsfunctions>`.\n\nFor more details on the semantics of the online aggregation strategy arguments listed below, see :doc:`the Online Aggregation for Evals page</onlineagg>`.\n\n\n**batch_size** : int\n\tNumber of examples to process in one batch for GPU efficiency (if applicable)\n\n**preprocess_fn** : Callable\n\tUser-given function to preprocess a batch of examples; an eval config\'s RagSpec and PromptManager are input by the system\n\n**postprocess_fn** : Callable, optional\n\tUser-given function to postprocess a batch of examples and generations; a single cfg is passed as input by the system\n\n**compute_metrics_fn** : Callable\n\tUser-given evaluation function to compute eval metrics per batch\n\n**accumulate_metrics_fn** : Callable, optional\n\tUser-given evaluation function to aggregate algebraic eval metrics across batches. If this is not given, all metrics provided in :code:`eval_compute_metrics_fn` will be assumed to be distributive by default.\n\n**online_strategy_kwargs** : dict[str, Any], optional\n\tParameters for evals online aggregation strategy. The dictionary must include the following keys:\n\t\n\t* :code:`"strategy_name"` (str) - Must be :code:`"normal"`, :code:`"wilson"`, or :code:`"hoeffding"`.\n\t* :code:`"confidence_level"` (float) - Confidence level for confidence intervals on metrics. Must be in [0,1]. Default is 0.95 (95%).\n\t* :code:`"use_fpc"` (bool) - Whether to apply finite population correction. Default is :code:`True`.',
    '.. code-block:: python\n\n    # Based on the FiQA Pinecone tutorial notebook\n    spec = ServerlessSpec(cloud="gcp", region="us-central1")\n\n    # Create mode\n    vector_store_cfg_create={\n        "type": "pinecone",\n        "pinecone_api_key": PINECONE_API_KEY, # Or set the PINECONE_API_KEY environment variable\n        "spec": spec,\n        "metric": "cosine",\n        "batch_size": 1024, # documents are embedded in batches of 1024. Defaults to 128.\n    }\n\n    # Read and Update mode\n    vector_store_cfg_read_update={\n        "type": "pinecone", # Required\n        "pinecone_api_key": PINECONE_API_KEY, # Or set the PINECONE_API_KEY environment variable\n        "index_namespace": List([("fiqa", "chunk64"), ("fiqa", "chunk256")]), # Names of *pre-existing* pinecone indexes paired with respective namespaces\n        "embedding_cfg": {\n            "class": HuggingFaceEmbeddings,\n            "model_name": "sentence-transformers/all-MiniLM-L6-v2",\n            "model_kwargs": {"device": "cuda:0"},\n            "encode_kwargs": {"normalize_embeddings": True, "batch_size": 128}\n        },\n        "text_key": "original_doctext", # Metadata field name for raw text in Pinecone; defaults to "text"\n    }\n\n    rag_gpu = RFLangChainRagSpec(\n        document_loader=DirectoryLoader(\n            ...\n        ),\n        ...\n        vector_store_cfg=vector_store_cfg_create,  # Using Pinecone in create mode\n    )',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.8252, 0.5741],
#         [0.8252, 1.0000, 0.6055],
#         [0.5741, 0.6055, 1.0000]])

Training Details

Training Dataset

Unnamed Dataset

Size: 444 training samples
Columns: sentence_0, sentence_1, and label
Approximate statistics based on the first 444 samples:
sentence_0 sentence_1 label
type string string float
details
min: 15 tokens
mean: 41.97 tokens
max: 70 tokens
min: 36 tokens
mean: 225.67 tokens
max: 256 tokens
min: 0.0
mean: 0.25
max: 1.0

Samples:

sentence_0	sentence_1	label
`How do the Stop and Delete IC Ops compare in terms of their effects on a run's state, visibility on the dashboard, resource usage, artifact preservation, and what further IC Ops can be performed on the run afterward?`	Delete[object Object]----[object Object][object Object]This IC Op earmarks the run to be deleted from the next chunk onward. [object Object]On the chart, you will see its curves vanish almost immediately. [object Object]You cannot do any further IC Ops on a deleted run because it will not be visible. [object Object]Note that although a deleted run vanishes from the plots, its model checkpoints are still part of [object Object]the artifacts of that experiment so that you have post-hoc audibility.[object Object]	`1.0`
`How does RapidFire AI's approach to multi-config experimentation unify training (run_fit) and evaluation (run_evals) workflows under a common adaptive execution model, and what are the key differences in how each workflow exposes parallelism controls, return values, and user-provided functions?`	`RFOpenAIAPIModelConfig`

This is a wrapper around OpenAI's API client config and chat completion parameters. The full list of their arguments are available on this page <https://platform.openai.com/docs/api-reference/chat/create>__.

The difference here is that the individual arguments (knobs) can be :class:List valued or :class:Range valued in an :class:RFOpenAIAPIModelConfig. That is how you can specify a base set of knob combinations from which a config group can be produced. Also read :doc:the Multi-Config Specification page</configs>.

.. py:class:: RFOpenAIAPIModelConfig

:param client_config: A dictionary necessary for initializing the AsyncOpenAI client. All knobs given in this dictionary are simply passed to the AsyncOpenAI client as is. We recommend listing at least the following knobs.

* :code:`"api_key"`: Your OpenAI API key for authentication. Note that we are NOT able to provide a publicly visible API key.
* :code:`"max_retries"`: Maximum ...</code> | <code>0.0</code> |

How do RFvLLMModelConfig and RFOpenAIAPIModelConfig compare in terms of their configuration parameters, underlying systems, rate limiting capabilities, and typical use cases?

RapidFire AI's execution pipeline for RAG pipelines engineering is split into 2 main stages as illustrated in the figure below:


[object Object]
[object Object]
[object Object]

[object Object] This creates both Document Preprocessing workers and Query Processing workers. Provide :code:[object Object], optional :code:[object Object], :code:[object Object], and optional :code:[object Object]. The document preprocessing w... | 0.0 |

Loss: ContrastiveLoss with these parameters:

json
1{ 2 "distance_metric": "SiameseDistanceMetric.COSINE_DISTANCE", 3 "margin": 0.5, 4 "size_average": true 5}

Training Hyperparameters

Non-Default Hyperparameters

per_device_train_batch_size: 16
per_device_eval_batch_size: 16
num_train_epochs: 1
multi_dataset_batch_sampler: round_robin

All Hyperparameters

Click to expand

do_predict: False
prediction_loss_only: True
per_device_train_batch_size: 16
per_device_eval_batch_size: 16
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: 1
max_steps: -1
lr_scheduler_type: linear
lr_scheduler_kwargs: None
warmup_ratio: None
warmup_steps: 0
log_level: passive
log_level_replica: warning
log_on_each_node: True
logging_nan_inf_filter: True
enable_jit_checkpoint: False
save_on_each_node: False
save_only_model: False
restore_callback_states_from_checkpoint: False
use_cpu: False
seed: 42
data_seed: None
bf16: False
fp16: False
bf16_full_eval: False
fp16_full_eval: False
tf32: None
local_rank: -1
ddp_backend: None
debug: []
dataloader_drop_last: False
dataloader_num_workers: 0
dataloader_prefetch_factor: None
disable_tqdm: False
remove_unused_columns: True
label_names: None
load_best_model_at_end: False
ignore_data_skip: False
fsdp: []
fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
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
group_by_length: False
length_column_name: length
project: huggingface
trackio_space_id: trackio
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
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_for_metrics: []
eval_do_concat_batches: True
auto_find_batch_size: False
full_determinism: False
ddp_timeout: 1800
torch_compile: False
torch_compile_backend: None
torch_compile_mode: None
include_num_input_tokens_seen: no
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: True
use_cache: False
prompts: None
batch_sampler: batch_sampler
multi_dataset_batch_sampler: round_robin
router_mapping: {}
learning_rate_mapping: {}

Training Time

Training: 5.2 seconds

Framework Versions

Python: 3.12.13
Sentence Transformers: 5.4.1
Transformers: 5.0.0
PyTorch: 2.10.0+cu128
Accelerate: 1.13.0
Datasets: 4.0.0
Tokenizers: 0.22.2

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",
}

ContrastiveLoss

bibtex

@inproceedings{hadsell2006dimensionality,
    author={Hadsell, R. and Chopra, S. and LeCun, Y.},
    booktitle={2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06)},
    title={Dimensionality Reduction by Learning an Invariant Mapping},
    year={2006},
    volume={2},
    number={},
    pages={1735-1742},
    doi={10.1109/CVPR.2006.100}
}

	sentence_0	sentence_1	label
type	string	string	float
details	min: 15 tokens mean: 41.97 tokens max: 70 tokens	min: 36 tokens mean: 225.67 tokens max: 256 tokens	min: 0.0 mean: 0.25 max: 1.0