xnetba/train-test

1import gradio as gr2 3# Initialize your model: Use the Hugging Face library to initialize your model with the chosen pre-trained model architecture4from transformers import BertForSequenceClassification5model = BertForSequenceClassification.from_pretrained("bert-base-uncased", num_labels=2)6 7#Tokenize your data: Tokenize your input data using the tokenizer provided by Hugging Face for the specific model you're using. 8#This step converts text inputs into numerical representations that the model can process.9from transformers import BertTokenizer10tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")11 12#Tokenize the input text13text = "Hello, how are you?"14tokens = tokenizer.encode(text, add_special_tokens=True)15 16#Convert tokens to input IDs17input_ids = tokenizer.convert_tokens_to_ids(tokens)18 19#Attention masks20attention_mask = tokenizer.create_attention_mask(input_ids)21 22#Create data loaders: Create data loaders or data iterators to efficiently load and batch your tokenized data during training. 23#Hugging Face provides tools like DataLoader or DataProcessor for this purpose.24from transformers import DataLoader25 26#Prepare your tokenized data and Create a dataset27from torch.utils.data import TensorDataset28dataset = TensorDataset(input_ids, attention_mask, labels)29 30#Create a data loader31batch_size = 3232shuffle = True33data_loader = DataLoader(dataset, batch_size=batch_size, shuffle=shuffle)34 35#Iterate through the data loader and perform training step using the batched data36for batch in data_loader:37    input_ids_batch, attention_mask_batch, labels_batch = batch38 39#Define your training loop: Write the training loop using PyTorch or TensorFlow, depending on the framework supported by the Hugging Face model you are using. 40#Within the loop, you'll need to define the loss function, optimizer, and any additional metrics you want to track.41import torch42import torch.nn as nn43import torch.optim as optim44 45learning_rate = 0.00146optimizer = optim.Adam(model.parameters(), lr=learning_rate)47 48#Fine-tune the model: Train the model on your dataset using the training loop. 49#Adjust the hyperparameters such as learning rate, batch size, and number of epochs to optimize performance. 50#Monitor the validation set metrics to avoid overfitting and select the best model based on these metrics.51 52 53#Evaluate the model: Once training is complete, evaluate the performance of your trained model on the test set. Calculate relevant metrics such as accuracy, precision, recall, or F1 score.54#Save and load the model: Save the trained model parameters to disk so that you can later load and use it for predictions without having to retrain from scratch.55 56def greet(name):57    return "Hello " + name + "!!"58 59iface = gr.Interface(fn=greet, inputs="text", outputs="text")60iface.launch()