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import gradio as gr
import math
import spacy
from datasets import load_dataset
from sentence_transformers import SentenceTransformer
from sentence_transformers import InputExample
from sentence_transformers import losses
from transformers import AutoTokenizer, AutoModel, AutoModelForSequenceClassification
from transformers import TrainingArguments, Trainer
import torch
import torch.nn.functional as F
from torch.utils.data import DataLoader
import numpy as np
import evaluate
import nltk
from nltk.corpus import stopwords
import subprocess
import sys
from transformers import DataCollatorWithPadding
# !pip install https://huggingface.co/spacy/en_core_web_sm/resolve/main/en_core_web_sm-any-py3-none-any.whl
# subprocess.check_call([sys.executable, '-m', 'pip', 'install', 'https://huggingface.co/spacy/en_core_web_sm/resolve/main/en_core_web_sm-any-py3-none-any.whl'])
# tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/all-MiniLM-L6-v2')
# data_collator = DataCollatorWithPadding(tokenizer=tokenizer)
# nltk.download('stopwords')
# nlp = spacy.load("en_core_web_sm")
# stops = stopwords.words("english")
# answer = "Pizza"
guesses = []
answer = "Pizza"
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# def normalize(comment, lowercase, remove_stopwords):
# if lowercase:
# comment = comment.lower()
# comment = nlp(comment)
# lemmatized = list()
# for word in comment:
# lemma = word.lemma_.strip()
# if lemma:
# if not remove_stopwords or (remove_stopwords and lemma not in stops):
# lemmatized.append(lemma)
# return " ".join(lemmatized)
# def tokenize_function(examples):
# return tokenizer(examples["text"], truncation=True)
def compute_metrics(eval_pred):
logits, labels = eval_pred
predictions = np.argmax(logits, axis=-1)
metric = evaluate.load("accuracy")
return metric.compute(predictions=predictions, references=labels)
def training():
dataset_id = "ag_news"
print("GETTING DATASET")
dataset = load_dataset(dataset_id)
# dataset = dataset["train"]
# tokenized_datasets = dataset.map(tokenize_function, batched=True)
print(f"- The {dataset_id} dataset has {dataset['train'].num_rows} examples.")
print(f"- Each example is a {type(dataset['train'][0])} with a {type(dataset['train'][0]['text'])} as value.")
print(f"- Examples look like this: {dataset['train'][0]}")
# small_train_dataset = tokenized_datasets["train"].shuffle(seed=42).select(range(1000))
# small_eval_dataset = tokenized_datasets["test"].shuffle(seed=42).select(range(1000))
# dataset = dataset["train"].map(tokenize_function, batched=True)
# dataset.set_format(type="torch", columns=["input_ids", "token_type_ids", "attention_mask", "label"])
# dataset.format['type']
# tokenized_news = dataset.map(tokenize_function, batched=True)
# model = AutoModelForSequenceClassification.from_pretrained("sentence-transformers/all-MiniLM-L6-v2", num_labels=2)
# print(dataset)
train_examples = []
train_data = dataset["train"]
# For agility we only 1/2 of our available data
n_examples = dataset["train"].num_rows // 2
# n_remaining = dataset["train"].num_rows - n_examples
# dataset_clean = {}
# # dataset_0 = []
# # dataset_1 = []
# # dataset_2 = []
# # dataset_3 = []
# for i in range(n_examples):
# dataset_clean[i] = {}
# dataset_clean[i]["text"] = normalize(train_data[i]["text"], lowercase=True, remove_stopwords=True)
# dataset_clean[i]["label"] = train_data[i]["label"]
# if train_data[i]["label"] == 0:
# dataset_0.append(dataset_clean[i])
# elif train_data[i]["label"] == 1:
# dataset_1.append(dataset_clean[i])
# elif train_data[i]["label"] == 2:
# dataset_2.append(dataset_clean[i])
# elif train_data[i]["label"] == 3:
# dataset_3.append(dataset_clean[i])
# n_0 = len(dataset_0) // 2
# n_1 = len(dataset_1) // 2
# n_2 = len(dataset_2) // 2
# n_3 = len(dataset_3) // 2
# print("Label lengths:", len(dataset_0), len(dataset_1), len(dataset_2), len(dataset_3))
for i in range(n_examples):
example = train_data[i]
# example_opposite = dataset_clean[-(i)]
# print(example["text"])
train_examples.append(InputExample(texts=[example['text']], label=example['label']))
# for i in range(n_0):
# example = dataset_0[i]
# # example_opposite = dataset_0[-(i)]
# # print(example["text"])
# train_examples.append(InputExample(texts=[example['text']], label=0))
# for i in range(n_1):
# example = dataset_1[i]
# # example_opposite = dataset_1[-(i)]
# # print(example["text"])
# train_examples.append(InputExample(texts=[example['text']], label=1))
# for i in range(n_2):
# example = dataset_2[i]
# # example_opposite = dataset_2[-(i)]
# # print(example["text"])
# train_examples.append(InputExample(texts=[example['text']], label=2))
# for i in range(n_3):
# example = dataset_3[i]
# # example_opposite = dataset_3[-(i)]
# # print(example["text"])
# train_examples.append(InputExample(texts=[example['text']], label=3))
train_dataloader = DataLoader(train_examples, shuffle=True, batch_size=25)
print("END DATALOADER")
# print(train_examples)
embeddings = finetune(train_dataloader)
return (dataset['train'].num_rows, type(dataset['train'][0]), type(dataset['train'][0]['text']), dataset['train'][0], embeddings)
def finetune(train_dataloader):
# model = AutoModelForSequenceClassification.from_pretrained("bert-base-cased", num_labels=5)
model_id = "sentence-transformers/all-MiniLM-L6-v2"
model = SentenceTransformer(model_id)
device = torch.device('cuda:0')
model = model.to(device)
# training_args = TrainingArguments(output_dir="test_trainer")
# USE THIS LINK
# https://huggingface.co/blog/how-to-train-sentence-transformers
train_loss = losses.BatchHardSoftMarginTripletLoss(model=model)
print("BEGIN FIT")
model.fit(train_objectives=[(train_dataloader, train_loss)], epochs=10)
model.save("ag_news_model")
model.save_to_hub("smhavens/all-MiniLM-agNews")
# accuracy = compute_metrics(eval, metric)
# training_args = TrainingArguments(output_dir="test_trainer", evaluation_strategy="epoch")
# trainer = Trainer(
# model=model,
# args=training_args,
# train_dataset=train,
# eval_dataset=eval,
# compute_metrics=compute_metrics,
# )
# trainer.train()
# sentences = ["This is an example sentence", "Each sentence is converted"]
# # model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')
# embeddings = model.encode(sentences)
# print(embeddings)
# # Sentences we want sentence embeddings for
# sentences = ['This is an example sentence', 'Each sentence is converted']
# # Load model from HuggingFace Hub
# # tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/all-MiniLM-L6-v2')
# # model = AutoModel.from_pretrained('sentence-transformers/all-MiniLM-L6-v2')
# # Tokenize sentences
# encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# # Compute token embeddings
# with torch.no_grad():
# model_output = model(**encoded_input)
# # Perform pooling
# sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
# # Normalize embeddings
# sentence_embeddings = F.normalize(sentence_embeddings, p=2, dim=1)
# print("Sentence embeddings:")
# print(sentence_embeddings)
return 0
def greet(name):
return "Hello " + name + "!!"
def check_answer(guess:str):
global guesses
global answer
guesses.append(guess)
output = ""
for guess in guesses:
output += ("- " + guess + "\n")
output = output[:-1]
if guess.lower() == answer.lower():
return "Correct!", output
else:
return "Try again!", output
def main():
print("BEGIN")
word1 = "Black"
word2 = "White"
word3 = "Sun"
global answer
answer = "Moon"
global guesses
num_rows, data_type, value, example, embeddings = training()
# prompt = f"{word1} is to {word2} as {word3} is to ____"
# with gr.Blocks() as iface:
# gr.Markdown(prompt)
# with gr.Tab("Guess"):
# text_input = gr.Textbox()
# text_output = gr.Textbox()
# text_button = gr.Button("Submit")
# with gr.Accordion("Open for previous guesses"):
# text_guesses = gr.Textbox()
# with gr.Tab("Testing"):
# gr.Markdown(f"""Number of rows in dataset is {num_rows}, with each having type {data_type} and value {value}.
# An example is {example}.
# The Embeddings are {embeddings}.""")
# text_button.click(check_answer, inputs=[text_input], outputs=[text_output, text_guesses])
# # iface = gr.Interface(fn=greet, inputs="text", outputs="text")
# iface.launch()
if __name__ == "__main__":
main() |