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i-dot-ai/
all-miniLM-L6-v2-UKPGA-6k-finetune

Name: all-miniLM-L6-v2-UKPGA-6k-finetune
Availability: InStock

sentence-transformers

text-embeddings-inference

endpoints_compatible

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all-miniLM-L6-v2-UKPGA-6k-finetune

This is a sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.

This model aims to provide an embedding finetuned to UK legislative language.

The model is used in an i-dot-ai project to give better semantic searching results for legislation.

The model has been finetuned on 6k question answer pairs from UK public general acts. The question and answer pairs can be found here: (i-dot-ai/legislation_2021-2023_qa)

The answers are sections from UK public general acts from 2010-2023. The matching questions are generated by gpt-4o.

The model performs similarly well to text-embedding-3-large. The finetuning has increased performance by 12% against it's base model all-miniLM-L6-v2.

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:

python

from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]

model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)

Evaluation Results

The model was evaluated against 1000 question and answer pairs from UKPGA 2010-2023 with questions generated by gpt-4o.

For an automated evaluation of this model, see the Sentence Embeddings Benchmark: https://seb.sbert.net

Training

The model was trained with the parameters:

DataLoader:

torch.utils.data.dataloader.DataLoader of length 1734 with parameters:

{'batch_size': 10, 'sampler': 'torch.utils.data.sampler.SequentialSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}

Loss:

sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss with parameters:

{'scale': 20.0, 'similarity_fct': 'cos_sim'}

Parameters of the fit()-Method:

{
    "epochs": 2,
    "evaluation_steps": 50,
    "evaluator": "sentence_transformers.evaluation.InformationRetrievalEvaluator.InformationRetrievalEvaluator",
    "max_grad_norm": 1,
    "optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
    "optimizer_params": {
        "lr": 2e-05
    },
    "scheduler": "WarmupLinear",
    "steps_per_epoch": null,
    "warmup_steps": 346,
    "weight_decay": 0.01
}

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
  (2): Normalize()
)

i-dot-ai/all-miniLM-L6-v2-UKPGA-6k-finetune