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/final_golden_rag_tuned_minilm")
# Run inference
sentences = [
    "How do you set up and run an SFT fine-tuning experiment from scratch using RapidFire AI's full installation, from installing the package through launching training and monitoring results?",
    '`see its details on Hugging Face <https://huggingface.co/datasets/trl-lib/ultrafeedback_binarized>`__.\nWe use a sample of 500 training examples for tractable demo runtimes. ',
    'Normal Approximation\n^^^^^^^^^^^^^^^^^^^\n\nThis is the default strategy, and it uses the Central Limit Theorem. \nIt is suitable for most cases with non-trivial sample sizes (n > 30). \nIt provides tight intervals when the statistical assumptions hold.\n\n* For algebraic metrics:\n\n.. math::\n\n   \\text{SE}_{\\hat{p}} = \\sqrt{\\frac{\\hat{p}(1-\\hat{p})}{n}} \\times \\text{FPC}\n\n   \\text{CI} = \\hat{p} \\pm 1.96 \\cdot \\text{SE}_{\\hat{p}}\n\n\n* For distributive metrics: \n\nEstimate population total :math:`\\widehat{T} = N\\bar{X}` with \nvariance :math:`\\text{Var}(\\widehat{T}) = N^2 \\cdot \\bar{X}(1-\\bar{X})/n` (FPC-adjusted).\n\n\nWilson Score\n^^^^^^^^^^^\n\nThis strategy is better for small sample sizes or metrics near 0/1 boundaries. \nIt is more robust than Normal Approximation for extreme proportions. \n\n* For algebraic metrics:\n\n.. math::\n\n   \\text{center} = \\frac{\\hat{p} + z^2/(2n_{\\text{eff}})}{1 + z^2/n_{\\text{eff}}}\n\n   \\text{margin} = \\frac{z\\sqrt{\\hat{p}(1-\\hat{p})/n_{\\text{eff}} + z^2/(4n_{\\text{eff}}^2)}}{1 + z^2/n_{\\text{eff}}}\n\nwhere :math:`n_{\\text{eff}} = n/\\text{FPC}^2` when using FPC. \nThe Wilson confidence interval is then :math:`[\\text{center} - \\text{margin}, \\text{center} + \\text{margin}]`,\nclamped to [0, 1].\n\n* For distributive metrics, this falls back to Normal Approximation. ',
]
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, 0.3247, 0.1786],
#         [0.3247, 1.0000, 0.2157],
#         [0.1786, 0.2157, 1.0000]])

Training Details

Training Dataset

Unnamed Dataset

Size: 208 training samples
Columns: sentence_0, sentence_1, and label
Approximate statistics based on the first 208 samples:
sentence_0 sentence_1 label
type string string float
details
min: 25 tokens
mean: 38.85 tokens
max: 70 tokens
min: 58 tokens
mean: 223.85 tokens
max: 256 tokens
min: 0.0
mean: 0.25
max: 1.0

Samples:

sentence_0	sentence_1	label
`How do you set up and use Pinecone as an external vector store in RapidFire AI's RAG pipeline, including configuring create, read, and update modes?`	`External Vector Stores: Pinecone and PGVector`

RapidFire AI also supports external persistent vector stores beyond the default in-memory FAISS. This allows you to scale to larger corpora, persist indexes across runs and experiments, and leverage managed vector DBMS services. As of this writing, Pinecone (hosted serverless or pod-based) and PostgreSQL PGVector (self-hosted or managed) are supported.

Each external store supports three modes of operation:

Create mode: Build a new index from base documents from within RapidFire AI itself and use it for RAG.
Read mode: Retrieve from a pre-existing index and use it for RAG.
Update mode: Add new content to an existing index from additional base documents from within RapidFire AI itself and use it for RAG.

See the :doc:API: LangChain RAG Spec page</ragspecs> for more details on how to specify these external vector stores.

The FiQA RAG tutorial notebooks have also been extended to showcase the external ... | `1.0` | | `How does the run_fit() workflow for SFT training use the create_model_fn and formatting_func together to prepare models and data, and how does this compare to the run_evals() workflow's use of preprocess_fn and the generator config?` | `Formatting Function`


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

`:param row: Dictionary containing a single data example with keys like "instruction"...` | `1.0` | | `How do you set up and use Pinecone as an external vector store in RapidFire AI's RAG pipeline, including configuring create, read, and update modes?` | `Run Fit`


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

:param eval_dataset: Evaluation dataset to measure eval metrics
:type eval_dataset: Dat...

| 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: 2.8 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