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_mnr_100")
# Run inference
sentences = [
    'How do you select specific GPUs for RapidFire AI to use, and how do you resolve port conflicts when starting the server?',
    'just start rapidfireai again with the above command.\n\nIf the start command fails for whatever reason, wait for half a minute and rerun it.\nFor diagnostics and common fixes (including Linux/macOS and Windows steps), see :doc:`Troubleshooting </troubleshooting>`.\n',
    'Step 7: End experiment; stop server when done\n------\n\nRun the cell to end the expeirment when you are done with it.\n\n.. code-block:: python\n\n   experiment.end()\n\nYou can then move on to another (named) experiment in the same session. \nRun as many experiments as you like; each will have its plots apppear on the dashboard \nunder its name. \nAll experiment artifacts (metrics files, logs, checkpoints, etc.) are *persistent* on \nyour machine in the same location as your notebook.\n\nWhen you are done overall, gracefully stop the RapidFire AI session and free the ports used in one of two ways:\n\n* Press Ctrl+C on the terminal where :code:`rapidfireai start` was performed. Wait for all services to finish cleanly. \n\n* In a separate terminal tab, run the stop command as follows and wait for it to finish fully. If you had run the start command as a background process, feel free to run the stop command in the same terminal tab.\n\n.. code-block:: bash\n\n   rapidfireai stop\n\n.. important::\n  If you kill the rapidfireai server forcibly without graceful stopping as above, you might lose some experiment artifacts and/or metadata.\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, 0.6904, 0.3372],
#         [0.6904, 1.0000, 0.4177],
#         [0.3372, 0.4177, 1.0000]])

Training Details

Training Dataset

Unnamed Dataset

Size: 52 training samples
Columns: sentence_0 and sentence_1
Approximate statistics based on the first 52 samples:
sentence_0 sentence_1
type string string
details
min: 25 tokens
mean: 38.85 tokens
max: 70 tokens
min: 58 tokens
mean: 223.25 tokens
max: 256 tokens

Samples:

sentence_0 sentence_1

What user-provided functions can be included in an eval config for run_evals(), and which are mandatory vs. optional? API: User-Provided Functions for Run Evals[object Object]===============[object Object][object Object]Users can provide the following custom functions as part of their eval config to be used in :func:[object Object].[object Object]Note that each leaf config can have its own set of functions for all of these.[object Object][object Object][object Object]Preprocess Function[object Object]-------------------[object Object][object Object]Mandatory user-provided function to prepare the inputs to be given to the generator model. [object Object]It is invoked for each batch during the evaluation process before generation.[object Object]Pass it directly to the :code:[object Object] key in your eval config dictionary.[object Object][object Object]The system injects into this function the batch data, as well as the RAG spec and [object Object]the prompt manager of an individual leaf config.[object Object][object Object][object Object].. py:function:: preprocess_fn(batch: dict[str, list], rag: RFLangChainRagSpec, prompt_manager: RFPromptManager) -> dict[str, list][object Object][object Object] :param batch: Dictionary with a batch of examples with dataset field names as keys and lists as values

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? :param vector_store_cfg: The vector store type and args to store and possibly index embedding vectors for retrieval, provided as a single dictionary.

    - :code:`"type"`: The type of vector store to use. Must be one of :code:`"faiss"`, :code:`"pgvector"`, or :code:`"pinecone"`. Required.
    - :code:`"batch_size"`: Number of vectors per insert batch. Applies to all 3 types of stores. Optional; default is 128.

    The remaining keys are type-specific args as listed below. The vector store operates in one of 3 modes depending on the rest of the RAG spec:

    - **Create mode:** When :code:`document_loader` is provided and no pre-existing index/collection names are specified, a new vector store is *created* and populated from the loaded documents.
    - **Read mode:** When :code:`document_loader` is absent and pre-existing index/collection names are specified, the vector store is opened in *read-only* mode for retrieval against the existing index.
    -...</code> |

How do you access the Interactive Control (IC) Ops panel from within a Jupyter notebook for both run_fit() and run_evals() workflows?

As of this writing, we support 4 IC Ops: [object Object], [object Object], [object Object], and [object Object].
We explain each shortly below.


[object Object]
[object Object]

Under the hood, RapidFire AI automat... |

Loss: MultipleNegativesRankingLoss with these parameters:

json
1{ 2 "scale": 20.0, 3 "similarity_fct": "cos_sim", 4 "gather_across_devices": false, 5 "directions": [ 6 "query_to_doc" 7 ], 8 "partition_mode": "joint", 9 "hardness_mode": null, 10 "hardness_strength": 0.0 11}

Training Hyperparameters

Non-Default Hyperparameters

per_device_train_batch_size: 16
per_device_eval_batch_size: 16
num_train_epochs: 100
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: 100
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: 1.0 minutes

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",
}

MultipleNegativesRankingLoss

bibtex

@misc{oord2019representationlearningcontrastivepredictive,
      title={Representation Learning with Contrastive Predictive Coding},
      author={Aaron van den Oord and Yazhe Li and Oriol Vinyals},
      year={2019},
      eprint={1807.03748},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/1807.03748},
}

	sentence_0	sentence_1
type	string	string
details	min: 25 tokens mean: 38.85 tokens max: 70 tokens	min: 58 tokens mean: 223.25 tokens max: 256 tokens