use crate::models;
use serde::{Deserialize, Serialize};
#[derive(Clone, Default, Debug, PartialEq, Serialize, Deserialize, bon::Builder)]
pub struct FineTuningJobCheckpointMetrics {
#[serde(rename = "step", skip_serializing_if = "Option::is_none")]
pub step: Option<f64>,
#[serde(rename = "train_loss", skip_serializing_if = "Option::is_none")]
pub train_loss: Option<f64>,
#[serde(
rename = "train_mean_token_accuracy",
skip_serializing_if = "Option::is_none"
)]
pub train_mean_token_accuracy: Option<f64>,
#[serde(rename = "valid_loss", skip_serializing_if = "Option::is_none")]
pub valid_loss: Option<f64>,
#[serde(
rename = "valid_mean_token_accuracy",
skip_serializing_if = "Option::is_none"
)]
pub valid_mean_token_accuracy: Option<f64>,
#[serde(rename = "full_valid_loss", skip_serializing_if = "Option::is_none")]
pub full_valid_loss: Option<f64>,
#[serde(
rename = "full_valid_mean_token_accuracy",
skip_serializing_if = "Option::is_none"
)]
pub full_valid_mean_token_accuracy: Option<f64>,
}
impl FineTuningJobCheckpointMetrics {
pub fn new() -> FineTuningJobCheckpointMetrics {
FineTuningJobCheckpointMetrics {
step: None,
train_loss: None,
train_mean_token_accuracy: None,
valid_loss: None,
valid_mean_token_accuracy: None,
full_valid_loss: None,
full_valid_mean_token_accuracy: None,
}
}
}
impl std::fmt::Display for FineTuningJobCheckpointMetrics {
fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
match serde_json::to_string(self) {
Ok(s) => write!(f, "{}", s),
Err(_) => Err(std::fmt::Error),
}
}
}