freedomtowin

let _some_obj = self.some_obj.clone();
tokio::task::spawn(async move {
  	process(_some_obj)
});

freedomtowin

#[derive(Debug, Clone)]
struct SomethingBig {
    name: String,
    children: Vec<SomethingBig>,
}

impl SomethingBig {
    fn modify(&mut self) {
        self.name = "modified".to_string();
    }

freedomtowin

let result: Result<f64, String> = divide_function(100.23, 73.98);
match result {
    Ok(x) => println!("Result: {}", x),
    Err(e) => println!("Error: {}", e),
}

freedomtowin

let result: Option<String> = Some("hello".to_string);
match result {
      Some(x) => println!("Result: {}", x),
      None => println!("Error"),
  }

freedomtowin

| Epoch | Training Loss | Validation Loss | Accuracy | F1       |
|-------|---------------|-----------------|----------|----------|
| 1     | No log        | 0.383960        | 0.830882 | 0.883249 |
| 2     | 0.507400      | 0.558366        | 0.845588 | 0.891192 |
| 3     | 0.259800      | 0.712663        | 0.865196 | 0.905009 |

TrainOutput(global_step=1377, training_loss=0.3134817651154429, metrics={'train_runtime': 241.2671, 'train_samples_per_second': 45.609, 'train_steps_per_second': 5.707, 'total_flos': 405114969714960.0, 'train_loss': 0.3134817651154429, 'epoch': 3.0})

freedomtowin

import numpy as np
from transformers import TrainingArguments
from transformers import Trainer
import evaluate
  
metric = evaluate.load("glue", "mrpc")

def compute_metrics(eval_preds):	
	logits, labels = eval_preds
	predictions = np.argmax(logits, axis=-1)

freedomtowin

stateDiagram-v2
Metrics --> Trainer
Hyperparameters --> Trainer
Model --> Trainer
Metrics --> Trainer
TrainingDataset --> Trainer
ValidationDataset --> Trainer
TestDataset --> Trainer
Tokenizer --> Trainer
DataCollator --> Trainer

freedomtowin

Epoch 1/3
  Training Loss: 0.5249
  Validation Loss: 0.4029
  Evaluation Metrics: {'accuracy': 0.8284313725490197, 'f1': 0.8741007194244604}
Epoch 2/3
  Training Loss: 0.2626
  Validation Loss: 0.3794
  Evaluation Metrics: {'accuracy': 0.8529411764705882, 'f1': 0.8979591836734694}
Epoch 3/3
  Training Loss: 0.0937

freedomtowin

from transformers import AutoModelForSequenceClassification
import torch
from transformers import AdamW
from transformers import get_scheduler
from tqdm.auto import tqdm
import evaluate

# Initialize metric and model
metric = evaluate.load("glue", "mrpc")
model = AutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels=2)

freedomtowin

torch.Size([16, 80])
torch.Size([16, 72])
torch.Size([16, 84])
torch.Size([16, 86])
torch.Size([16, 75])