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ronit01/final_golden_rag_tuned_minilm_contrastive_50epoch

sourceHugging Faceupdated 5mo agoView on Hugging Face

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SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2

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:

bash

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]])

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Downstream Usage (Sentence Transformers)

You can finetune this model on your own dataset.

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Training Details

Training Dataset

Unnamed Dataset

—Size: 276 training samples
—Columns: <code>sentence0</code>, <code>sentence1</code>, and <code>label</code>
—Approximate statistics based on the first 276 samples: | | sentence0 | sentence1 | label | |:--------|:-----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:---------------------------------------------------------------| | type | string | string | float | | details | <ul><li>min: 11 tokens</li><li>mean: 30.57 tokens</li><li>max: 48 tokens</li></ul> | <ul><li>min: 64 tokens</li><li>mean: 231.63 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.17</li><li>max: 1.0</li></ul> |
—Samples: | sentence0 | sentence1 | label | |:--------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------| | <code>How do you select specific GPUs for RapidFire AI to use, and how do you resolve port conflicts when starting the server?</code> | <code>Port conflicts (services already running) ---------------------------------------- If you encounter port conflicts, you can kill existing processes. .. code-block:: bash lsof -t -i:8852 \| xargs kill -9 # mlflow lsof -t -i:8851 \| xargs kill -9 # dispatcher lsof -t -i:8853 \| xargs kill -9 # frontend server Select specific GPU(s) to use ----------------------------- Set the `CUDA_VISIBLE_DEVICES environment variable BEFORE running rapidfireai start` to control which GPU(s) RapidFire can see and use. .. code-block:: bash export CUDAVISIBLEDEVICES=2 # use GPU index 2 only rapidfireai start Multiple GPUs (example: GPUs 0 and 2): .. code-block:: bash export CUDAVISIBLEDEVICES=0,2 rapidfireai start From a Python script (set before importing/starting RapidFire): .. code-block:: python import os os.environ["CUDAVISIBLEDEVICES"] = "2" # then start your RapidFire workflow </code> | <code>1.0</code> | | <code>How do you install and initialize RapidFire AI for fine-tuning workflows, and what steps are required to access gated Hugging Face models?</code> | <code>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.accumulatemetricsfn(aggregated_metrics: dict[str, list[dict[str, Any]]]) -> dict[str, dict[str, Any]]

:param aggregatedmetrics: 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...</code> | <code>0.0</code> | | <code>What are the two knob set generators currently supported by RapidFire AI for creating multi-config specifications?</code> | <code>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 `MLflow <https://mlflow.org/>`_, 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.

As of this writing, apart from MLflow, RapidFire AI also supports TensorBoard <https://www.tensorflow.org/tensorboard>_ and `Trackio <https://huggingface.co/docs/trackio/en/index>`_ for logging metrics plots. Specify any one, two, or all three dashboards to use with the following server start argument.

.. code-block:: bash

rapidfireai start --tracking-backends [mlflow | tensorboard | trackio]

Alternatively, set the dashboard using its environment variable as below in your python code/notebook:

.. code-block:: python

os.environ["RFMLFLOWENABLED"] = "true" os.environ["RFTENSORBOARDENABLED...</code> | <code>0.0</code> |

—Loss: <code>ContrastiveLoss</code> with these parameters:

json

  {
      "distance_metric": "SiameseDistanceMetric.COSINE_DISTANCE",
      "margin": 0.5,
      "size_average": true
  }

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

<details><summary>Click to expand</summary>

—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: {'minnumparams': 0, 'xla': False, 'xlafsdpv2': False, 'xlafsdpgrad_ckpt': False}
—accelerator_config: {'splitbatches': False, 'dispatchbatches': None, 'evenbatches': True, 'useseedablesampler': True, 'nonblocking': False, 'gradientaccumulationkwargs': None}
—parallelism_config: None
—deepspeed: None
—label_smoothing_factor: 0.0
—optim: adamwtorchfused
—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: {}

</details>

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}
}

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