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_contrastive_50epoch")
# Run inference
sentences = [
    'What are the two knob set generators currently supported by RapidFire AI for creating multi-config specifications?',
    'RapidFire AI offers a browser-based dashboard to automatically visualize all ML metrics and lets \nyou control runs on the fly from there. \nOur current default dashboard is a fork of the popular OSS tool `MLflow <https://mlflow.org/>`__, \nand it inherits much of MLflow\'s native features.\nThe dashboard URI is printed when the rapidfireai server is started; open it in a browser. \n\nAs of this writing, apart from MLflow, RapidFire AI also supports \n`TensorBoard  <https://www.tensorflow.org/tensorboard>`__\nand `Trackio <https://huggingface.co/docs/trackio/en/index>`__\nfor logging metrics plots. \nSpecify any one, two, or all three dashboards to use with the following server start argument. \n\n.. code-block:: bash\n\n   rapidfireai start --tracking-backends [mlflow | tensorboard | trackio]\n\nAlternatively, set the dashboard using its environment variable as below in your python code/notebook:\n\n.. code-block:: python\n\n   os.environ["RF_MLFLOW_ENABLED"] = "true"\n   os.environ["RF_TENSORBOARD_ENABLED"] = "true"\n   os.environ["RF_TRACKIO_ENABLED"] = "true"\n\nSupport for other popular dashboards such as Weights & Biases and CometML is coming soon. \nThe rest of this section explains the new features of our MLflow-fork dashboard.\nNote that these new features are not yet available on the other dashboards.',
    'Compute Metrics Function\n------\n\nOptional user-provided function specifying custom evaluation metrics based on the generated \noutputs and ground truth.\n\nIt is passed to the :code:`compute_metrics` argument of :class:`RFModelConfig`. \nAlso read: :doc:`the LoRA and Model Configs page</models>`.\nYou can create multiple variants of these functions and pass them all as a single \n:code:`List` to your :class:`RFModelConfig` to create a multi-config specification.\n\nThis function is invoked by the underlying HF trainer at a cadence controlled by the \n:code:`eval_strategy` and :code:`eval_steps` arguments.\nAlso read: :doc:`the Trainer Configs page</trainers>`.\n\n.. py:function:: fit.compute_metrics_fn(eval_preds: Tuple) -> Dict[str, float]\n\n   :param eval_preds: Tuple containing generated predictions and ground truth labels from the eval dataset.\n   :type eval_preds: Tuple[List[str], List[str]]\n\n   :return: Dictionary with user-defined metrics with names keys and numbers as values\n   :rtype: Dict[str, float]\n\n\n**Example:**\n\n.. code-block:: python\n\n\t# From the SFT tutorial notebook\n\tdef sample_compute_metrics(eval_preds):  \n\t\t"""Optional function to compute eval metrics based on predictions and labels"""\n\t\tpredictions, labels = eval_preds\n\n\t\t# Standard text-based eval metrics: Rouge and BLEU\n\t\timport evaluate\n\t\trouge = evaluate.load("rouge")\n\t\tbleu = evaluate.load("bleu")\n\n\t\trouge_output = rouge.compute(predictions=predictions, references=labels, use_stemmer=True)\n\t\trouge_l = rouge_output["rougeL"]\n\t\tbleu_output = bleu.compute(predictions=predictions, references=labels)\n\t\tbleu_score = bleu_output["bleu"]\n\n\t\treturn {"rougeL": round(rouge_l, 4), "bleu": round(bleu_score, 4)}',
]
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, 0.3398, 0.4086],
#         [0.3398, 1.0000, 0.6458],
#         [0.4086, 0.6458, 1.0000]])

Training Details

Training Dataset

Unnamed Dataset

Size: 276 training samples
Columns: sentence_0, sentence_1, and label
Approximate statistics based on the first 276 samples:
sentence_0 sentence_1 label
type string string float
details
min: 11 tokens
mean: 30.57 tokens
max: 48 tokens
min: 64 tokens
mean: 231.63 tokens
max: 256 tokens
min: 0.0
mean: 0.17
max: 1.0

Samples:

sentence_0	sentence_1	label
`How do you select specific GPUs for RapidFire AI to use, and how do you resolve port conflicts when starting the server?`	Port conflicts (services already running)[object Object]----------------------------------------[object Object][object Object]If you encounter port conflicts, you can kill existing processes.[object Object][object Object].. code-block:: bash[object Object][object Object] lsof -t -i:8852 \| xargs kill -9 # mlflow[object Object] lsof -t -i:8851 \| xargs kill -9 # dispatcher[object Object] lsof -t -i:8853 \| xargs kill -9 # frontend server[object Object][object Object]Select specific GPU(s) to use[object Object]-----------------------------[object Object][object Object]Set the [object Object] environment variable BEFORE running [object Object] to control which GPU(s) RapidFire can see and use.[object Object][object Object].. code-block:: bash[object Object][object Object] export CUDA_VISIBLE_DEVICES=2 # use GPU index 2 only[object Object] rapidfireai start[object Object][object Object]Multiple GPUs (example: GPUs 0 and 2):[object Object][object Object].. code-block:: bash[object Object][object Object] export CUDA_VISIBLE_DEVICES=0,2[object Object] rapidfireai start[object Object][object Object]From a Python script (set before importing/starting RapidFire):[object Object][object Object].. code-block:: python[object Object][object Object] import os[object Object] os.environ["CUDA_VISIBLE_DEVICES"] = "2"[object Object] # then start your RapidFire workflow[object Object]	`1.0`
`How do you install and initialize RapidFire AI for fine-tuning workflows, and what steps are required to access gated Hugging Face models?`	`Eval Accumulate Metrics Function`

Optional user-provided function to aggregate algebraic eval metrics across all batches of the data. If this function is not provided, all metrics returned by :func:eval.compute_metrics_fn() will be assumed to be distributive (i.e., summed across batches) by default. Use this function when metrics require (weighted) averaging or other custom dataset-wide aggregation logic.

It is invoked once at the very end of the evaluation process after all batches have been processed. Pass it directly to the :code:accumulate_metrics_fn key in your eval config dictionary.

.. py:function:: eval.accumulate_metrics_fn(aggregated_metrics: dict[str, list[dict[str, Any]]]) -> dict[str, dict[str, Any]]

:param aggregated_metrics: Dictionary with a metric's name as key and a list of per-batch metric dictionaries as values from across all data batches. Inside each value dictionary, at least the reserved key :code:"value" will exist t... | 0.0 | | What are the two knob set generators currently supported by RapidFire AI for creating multi-config specifications? |

RapidFire AI offers a browser-based dashboard to automatically visualize all ML metrics and lets
you control runs on the fly from there.
Our current default dashboard is a fork of the popular OSS tool [object Object]__,
and it inherits much of MLflow's native features.
The dashboard URI is printed when the rapidfireai server is started; open it in a browser.


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

os.environ["RF_MLFLOW_ENABLED"] = "true"
os.environ["RF_TENSORBOARD_ENABLED...

| 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: 50
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: 50
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 Logs

Epoch	Step	Training Loss
27.7778	500	0.0028

Training Time

Training: 2.5 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",
}

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: 11 tokens mean: 30.57 tokens max: 48 tokens	min: 64 tokens mean: 231.63 tokens max: 256 tokens	min: 0.0 mean: 0.17 max: 1.0