ronit01/rag_tuned_minilm_mnr_10

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:

pip install -U sentence-transformers

Then you can load this model and run inference.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("ronit01/rag_tuned_minilm_mnr_10")
# Run inference
sentences = [
    "What is the difference between distributive and algebraic metrics in RapidFire AI's online aggregation for evals?",
    'Types of Metrics\n-----------------------\n\nWe support 2 types of metrics based on their aggregation semantics: \n\n* **Distributive Metrics:** These are purely additive over a given set of data points. \n  \n  Examples: *count* of number of correct predictions; *sum* of output token lengths across queries.\n\n* **Algebraic Metrics:** These are averages or proportions over a given set of data points. They can be decomposed into components that are individually distributive. \n  \n  Examples: *precision*, which counts number of correct predictions and total number of data points separately and then divides them; *mean rouge-1*, which averages per-example rouge-1 values that assesses overlap of tokens between generated text and ground truth text.\n\nWhen you define an eval metric via :func:`evals.compute_metrics_fn()` and :func:`evals.accumulate_metrics_fn()`, \nyou must specify their type (algebraic or distributive) and value range as illustrated below. \nFor metrics without a type defined, they will be displayed *as is*, i.e., without projected \nestimates or confidence intervals.',
    '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.',
]
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, 0.6788, 0.1914],
#         [0.6788, 1.0000, 0.2678],
#         [0.1914, 0.2678, 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: 11 tokens mean: 24.87 tokens max: 34 tokens	min: 31 tokens mean: 216.15 tokens max: 256 tokens

• Samples:

  sentence_0	sentence_1
  Why does RapidFire AI argue that simply downsampling the evaluation data is insufficient compared to its online aggregation approach?	Why Not Just Downsample? ------------------------ One might wonder why downsampling the eval set does not suffice here. :doc:As also explained on this page</difference>, downsampling alone has significant disadvantages compared to the approach offered by RapidFire AI. First, you have to decide a downsample size upfront, which is not trivial if your eval metrics have high variance across examples. Point estimates without confidence intervals can give false confidence in a sample. You can resample manually over and over, but that adds manual grunt work of juggling separate samples/files. Finally, downsampling alone does not offer you the power of IC Ops and automated parallelization to try new configs on the fly--you'd have reimplement those manually. RapidFire AI's online aggregation approach with IC Ops avoids all the above issues, while also being complementary to downsampling, i.e., you can use both in conjunction for even lower runtimes/costs.
  What is online aggregation in the context of RapidFire AI evals, and what three confidence interval strategies does it support?	RapidFire AI transforms the status quo by adapting the powerful idea of online aggregation
  from database systems research to LLM evals.
  Our adaptive execution engine, :doc:as described on this page</difference>, automatically
  shards the data and processes multiple configs in parallel, one shard at a time, with
  efficient swapping techniques.

  This means you get running metric estimates with confidence intervals in real time. So, you can confidently stop poor configs earlier, clone better configs on the fly, and perform more informed exploration to reach much better eval metrics in much less time.

  Example: Traditional Batch Evals vs. RapidFire AI

  For instance, suppose you have an evals set with 400 queries. You decide to compare, say, 4 RAG configs in one go with RapidFire AI with number of shards set to 8. The illustration below contrasts traditional batch evals vs. RapidFire AI's approach for a simple eval metric.

  .. list-table:: :widths: 50 50 :class... | | What arguments does the RFModelConfig class accept for defining a model configuration in RapidFire AI? | RFModelConfig

  This is a core class in the RapidFire AI API that abstracts multiple Hugging Face APIs under the hood to simplify and unify all model-related specifications. In particular, it unifies model loading, training configurations, and LoRA settings into one class.

  It gives you flexibility to try out variations of LoRA adapter structures, training arguments for multiple control flows (SFT, DPO, and GRPO), formatting and metrics functions, and generation specifics.

  Some of the arguments (knobs) here can also be :class:List valued or :class:Range valued depending on its data type, as explained below. All this helps form the base set of knob combinations from which a config group can be produced. Also read :doc:the Multi-Config Specification page</configs>.

  .. py:class:: RFModelConfig

  :param model_name: Model identifier for use with Hugging Face's :code:AutoModel.from_pretrained(). Can be a Hugging Face model hub name (e.g., ``"Qwen/Qwen2.5-7B-Instruct... |

• Loss: MultipleNegativesRankingLoss with these parameters:

  {
      "scale": 20.0,
      "similarity_fct": "cos_sim",
      "gather_across_devices": false,
      "directions": [
          "query_to_doc"
      ],
      "partition_mode": "joint",
      "hardness_mode": null,
      "hardness_strength": 0.0
  }
  

Training Hyperparameters

Non-Default Hyperparameters

• per_device_train_batch_size: 16
• per_device_eval_batch_size: 16
• num_train_epochs: 10
• 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: 10
• 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: 6.1 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

@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

@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},
}