import random import matplotlib.pyplot as plt import nltk import numpy as np import pandas as pd import torch import torch.nn from nltk.tokenize import word_tokenize from torch.utils.data import DataLoader, RandomSampler, SequentialSampler from torch.utils.data import TensorDataset, random_split from transformers import DistilBertForSequenceClassification, AdamW from transformers import DistilBertTokenizer from transformers import get_linear_schedule_with_warmup nltk.download('punkt') # %matplotlib inline df = pd.read_csv('/content/train.csv') print(f'Number of training samples: {df.shape[0]}') df.sample(100) tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased') excerpts = df.excerpt.values targets = df.target.values.astype('float32') plt.hist(df['target']) plt.show() max_len = 0 for i in excerpts: input_ids = tokenizer.encode(i, add_special_tokens=True) max_len = max(max_len, len(input_ids)) print(max_len) input_ids = [] attention_masks = [] for i in excerpts: encoded_text = tokenizer.encode_plus( i, add_special_tokens=True, max_length=315, pad_to_max_length=True, return_attention_mask=True, return_tensors='pt' ) input_ids.append(encoded_text['input_ids']) attention_masks.append(encoded_text['attention_mask']) input_ids = torch.cat(input_ids, dim=0) attention_masks = torch.cat(attention_masks, dim=0) labels = torch.tensor(targets) labels = labels.float() # Combine the training inputs into a TensorDataset. dataset = TensorDataset(input_ids, attention_masks, labels) # Create a 80-20 train-validation split. # Calculate the number of samples to include in each set. train_size = int(0.8 * len(dataset)) val_size = len(dataset) - train_size # Divide the dataset by randomly selecting samples. train_dataset, val_dataset = random_split(dataset, [train_size, val_size]) print('{:>5,} training samples'.format(train_size)) print('{:>5,} validation samples'.format(val_size)) batch_size = 8 train_dataloader = DataLoader( train_dataset, sampler=RandomSampler(train_dataset), batch_size=batch_size ) validation_dataloader = DataLoader( val_dataset, sampler=SequentialSampler(val_dataset), batch_size=batch_size ) model = DistilBertForSequenceClassification.from_pretrained( "distilbert-base-uncased", num_labels=1, output_attentions=False, output_hidden_states=False ) torch.cuda.empty_cache() model.cuda() optimizer = AdamW(model.parameters(), lr=2e-5, eps=1e-8) EPOCHS = 4 total_steps = len(train_dataloader) * EPOCHS scheduler = get_linear_schedule_with_warmup(optimizer, num_warmup_steps=0, num_training_steps=total_steps) device = torch.device('cuda' if torch.cuda.is_available else 'cpu') seed = 42 random.seed(seed) np.random.seed(seed) torch.manual_seed(seed) torch.cuda.manual_seed_all(seed) training_stats = [] for epoch in range(EPOCHS): total_train_loss = 0 model.train() for step, batch in enumerate(train_dataloader): b_input_ids = batch[0].to(device) b_input_mask = batch[1].to(device) b_labels = batch[2].to(device) model.zero_grad() result = model(b_input_ids, attention_mask=b_input_mask, labels=b_labels, return_dict=True ) loss = result.loss logits = result.logits total_train_loss += loss.item() loss = loss.to(torch.float32) loss.backward() torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0) optimizer.step() scheduler.step() if step % 40 == 0: print(f'epoch: {epoch + 1} / {EPOCHS}, step {step + 1} / {len(train_dataloader)}, loss = {loss.item():.4f}') avg_train_loss = total_train_loss / len(train_dataloader) print(f'MSE {avg_train_loss:.2f}') print("Running Validation...") model.eval() total_eval_accuracy = 0 total_eval_loss = 0 nb_eval_steps = 0 for batch in validation_dataloader: b_input_ids = batch[0].to(device) b_input_mask = batch[1].to(device) b_labels = batch[2].to(device) with torch.no_grad(): result = model( b_input_ids, attention_mask=b_input_mask, labels=b_labels, return_dict=True, ) loss = loss.to(torch.float32) logits = result.logits total_eval_loss += loss.item() logits = logits.detach().cpu().numpy() label_ids = b_labels.to('cpu').numpy() avg_val_loss = total_eval_loss / len(validation_dataloader) print(f'Validation Loss {avg_val_loss:.2f}') training_stats.append( { 'epoch': epoch + 1, 'Training Loss': avg_train_loss, 'MSE': avg_val_loss, } ) print("") print("Training complete!") torch.save(model, '/content/untitled') PATH = '/content/pytorchBERTmodel' model = torch.load(PATH) model.eval() model.to(device) def predict(text, tokenizer): model.eval() model.to(device) def prepare_data(text, tokenizer): input_ids = [] attention_masks = [] encoded_text = tokenizer.encode_plus( text, add_special_tokens=True, max_length=315, padding=True, return_attention_mask=True, return_tensors='pt' ) input_ids.append(encoded_text['input_ids']) attention_masks.append(encoded_text['attention_mask']) input_ids = torch.cat(input_ids, dim=0) attention_masks = torch.cat(attention_masks, dim=0) return {'input_ids': input_ids, 'attention_masks': attention_masks} tokenized_example_text = prepare_data(text, tokenizer) with torch.no_grad(): result = model( tokenized_example_text['input_ids'].to(device), attention_mask=tokenized_example_text['attention_masks'].to(device), return_dict=True ).logits return result sen = """ Recent JWST observations suggest an excess of 𝑧 & 10 galaxy candidates above most theoretical models. Here, we explore how the interplay between halo formation timescales, star formation efficiency and dust attenuation affects the properties and number densities of galaxies we can detect in the early universe. We calculate the theoretical upper limit on the UV luminosity function, assuming star formation is 100% efficient and all gas in halos is converted into stars, and that galaxies are at the peak age for UV emission (∼ 10 Myr). This upper limit is ∼ 4 orders of magnitude greater than current observations, implying these are fully consistent with star formation in ΛCDM cosmology. One day, a woman was walking her two dogs. One was a big, friendly labrador and the other was a little yappy dog. As they walked, the little dog started to bark at a cat. The cat hissed and ran away. The labrador just stood there wagging his tail. The woman scolded the little dog, "You're supposed to be my protector! Why didn't you chase that cat away?" The labrador just looked at her and said, "I'm sorry, but I just don't see the point. """ sen_2 = """ Interstellar chemistry is important for galaxy formation, as it determines the rate at which gas can cool, and enables us to make predictions for observable spectroscopic lines from ions and molecules. We explore two central aspects of modelling the chemistry of the interstellar medium (ISM): (1) the effects of local stellar radiation, which ionises and heats the gas, and (2) the depletion of metals onto dust grains, which reduces the abundance of metals in the gas phase. We run high-resolution (400 M per baryonic particle) simulations of isolated disc galaxies, from dwarfs to Milky Way-mass, using the fire galaxy formation models together with the chimes non-equilibrium chemistry and cooling module. In our fiducial model, we couple the chemistry to the stellar fluxes calculated from star particles using an approximate radiative transfer scheme, and we implement an empirical density-dependent prescription for metal depletion. For comparison, we also run simulations with a spatially uniform radiation field, and without metal depletion. Our fiducial model broadly reproduces observed trends in Hi and H2 mass with stellar mass, and in line luminosity versus star formation rate for [Cii]158µm, [Oi]63µm, [Oiii]88µm, [Nii]122µm and Hα6563˚A. Our simulations """ windows_2 = [] words = word_tokenize(sen_2) for idx, text in enumerate(words): if idx <= len(words) - 21: x = ' '.join(words[idx: idx + 20]) windows_2.append(x) win_preds_2 = [] for text in windows_2: win_preds_2.append(predict(text, tokenizer).item()) windows = [] words = word_tokenize(sen) for idx, text in enumerate(words): if idx <= len(words) - 21: x = ' '.join(words[idx: idx + 20]) windows.append(x) win_preds = [] for text in windows: win_preds.append(predict(text, tokenizer).item()) plt.style.use('seaborn-notebook') # Data x = list(range(len(win_preds))) y = win_preds x2 = list(range(len(win_preds_2))) y2 = win_preds_2 # Plot plt.plot(x, y, color='#ff0000') plt.plot(x2, y2, color='blue') plt.grid(color='#cccccc', linestyle='--', linewidth=1) plt.xlabel('Window Sequence') plt.ylabel('Difficulty Score') plt.suptitle('Difficulty Score Over Time', fontsize=14, fontweight='bold') plt.show()