Custom Information Retrieval
Available at: https://huggingface.co/NASA-AIML/MIKA_Custom_IR
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
The model is custom trained on engineering documents for asymmetric infromation retrieval. It is intended to be used to identify engineering documents relevant to a query for use in design time. For example, a repository can be queried to find support for requirements or learn more about a specific type of failure.
Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
pip install -U sentence-transformers
Then you can use the model like this:
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer("NASA-AIML/MIKA_Custom_IR")
embeddings = model.encode(sentences)
print(embeddings)
Evaluation Results
This model was evaluated on three queries using precision at k for k=10,20, and 30. Mean average precision (MAP) was also calculated. The model was baselines against the pre-trained SBERT.
IR Method |
MAP |
|---|---|
Pre-trained sBERT |
0.648 |
Fine-tuned sBERT |
0.807 |
Training
The model was trained with the parameters:
DataLoader:
sentence_transformers.datasets.NoDuplicatesDataLoader.NoDuplicatesDataLoader of length 693 with parameters: .. code-block:: python
{‘batch_size’: 32}
Loss:
- sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss with parameters:
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
Parameters of the fit()-Method:
{
"epochs": 2,
"evaluation_steps": 100,
"evaluator": "sentence_transformers.evaluation.InformationRetrievalEvaluator.InformationRetrievalEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'transformers.optimization.AdamW'>",
"optimizer_params": {"lr": 2e-05},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 0,
"weight_decay": 0.01
}
Full Model Architecture
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: MPNetModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
(2): Normalize()
)