spark-bert 0.1.1

Hybrid vector search using an inverted index and BERT embeddings
Documentation 
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use anyhow::{Error as E, Result};
use candle_nn::VarBuilder;
use candle_transformers::models::bert::{BertModel, Config, HiddenAct, DTYPE};
use hf_hub::{api::sync::Api, Repo, RepoType};
use tokenizers::{Tokenizer, TruncationParams};

pub struct Args {
    /// Run on CPU rather than on GPU.
    pub cpu: bool,
    /// Enable tracing (generates a trace-timestamp.json file).
    pub tracing: bool,
    /// The model to use, check out available models: https://huggingface.co/models?library=sentence-transformers&sort=trending
    pub model_id: Option<String>,
    pub revision: Option<String>,
    /// Use the pytorch weights rather than the safetensors ones
    pub use_pth: bool,
    /// L2 normalization for embeddings.
    pub normalize_embeddings: bool,
    /// Use tanh based approximation for Gelu instead of erf implementation.
    pub approximate_gelu: bool,
}

impl Args {
    pub fn build_model_and_tokenizer(&self) -> Result<(BertModel, Tokenizer)> {
        let device = crate::util::device(self.cpu)?;
        let default_model = "sentence-transformers/all-MiniLM-L6-v2".to_string();
        let default_revision = "refs/pr/21".to_string();
        let (model_id, revision) = match (self.model_id.to_owned(), self.revision.to_owned()) {
            (Some(model_id), Some(revision)) => (model_id, revision),
            (Some(model_id), None) => (model_id, "main".to_string()),
            (None, Some(revision)) => (default_model, revision),
            (None, None) => (default_model, default_revision),
        };

        let repo = Repo::with_revision(model_id, RepoType::Model, revision);
        let (config_filename, tokenizer_filename, weights_filename) = {
            let api = Api::new()?;
            let api = api.repo(repo);
            let config = api.get("config.json")?;
            let tokenizer = api.get("tokenizer.json")?;
            let weights = if self.use_pth {
                api.get("pytorch_model.bin")?
            } else {
                api.get("model.safetensors")?
            };
            (config, tokenizer, weights)
        };
        let config = std::fs::read_to_string(config_filename)?;
        let mut config: Config = serde_json::from_str(&config)?;
        let mut tokenizer = Tokenizer::from_file(tokenizer_filename).map_err(E::msg)?;
        let _ = tokenizer.with_truncation(Some(TruncationParams {
            max_length: 512,
            ..Default::default()
        }));
        let vb = if self.use_pth {
            VarBuilder::from_pth(&weights_filename, DTYPE, &device)?
        } else {
            unsafe { VarBuilder::from_mmaped_safetensors(&[weights_filename], DTYPE, &device)? }
        };
        if self.approximate_gelu {
            config.hidden_act = HiddenAct::GeluApproximate;
        }
        let model = BertModel::load(vb, &config)?;
        Ok((model, tokenizer))
    }
}