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import gradio as gr
# Initialize your model: Use the Hugging Face library to initialize your model with the chosen pre-trained model architecture
from transformers import BertForSequenceClassification
model = BertForSequenceClassification.from_pretrained("bert-base-uncased", num_labels=2)
#Tokenize your data: Tokenize your input data using the tokenizer provided by Hugging Face for the specific model you're using.
#This step converts text inputs into numerical representations that the model can process.
from transformers import BertTokenizer
tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
#Tokenize the input text
text = "Hello, how are you?"
tokens = tokenizer.encode(text, add_special_tokens=True)
#Convert tokens to input IDs
input_ids = tokenizer.convert_tokens_to_ids(tokens)
#Attention masks
attention_mask = tokenizer.create_attention_mask(input_ids)
#Create data loaders: Create data loaders or data iterators to efficiently load and batch your tokenized data during training.
#Hugging Face provides tools like DataLoader or DataProcessor for this purpose.
from transformers import DataLoader
#Prepare your tokenized data and Create a dataset
from torch.utils.data import TensorDataset
dataset = TensorDataset(input_ids, attention_mask, labels)
#Create a data loader
batch_size = 32
shuffle = True
data_loader = DataLoader(dataset, batch_size=batch_size, shuffle=shuffle)
#Iterate through the data loader and perform training step using the batched data
for batch in data_loader:
input_ids_batch, attention_mask_batch, labels_batch = batch
#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.
#Within the loop, you'll need to define the loss function, optimizer, and any additional metrics you want to track.
import torch
import torch.nn as nn
import torch.optim as optim
learning_rate = 0.001
optimizer = optim.Adam(model.parameters(), lr=learning_rate)
#Fine-tune the model: Train the model on your dataset using the training loop.
#Adjust the hyperparameters such as learning rate, batch size, and number of epochs to optimize performance.
#Monitor the validation set metrics to avoid overfitting and select the best model based on these metrics.
#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.
#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.
def greet(name):
return "Hello " + name + "!!"
iface = gr.Interface(fn=greet, inputs="text", outputs="text")
iface.launch() |