app.py

#!pip install -q transformers datasets torch gradio console_logging numpy

import gradio as gr

title = 'Text market sentiment'
text_ = "Bitcoin to the moon"
model = torch.load("huggingface/Berbex/Model")

inp = [gr.Textbox(label='API Key', placeholder="What is your API Key?"), gr.Textbox(label='Audio File URL', placeholder="Audio file URL?")]
out = gr.Textbox(label='Output')

interface = gr.Interface.load(input=inp,output=out,
title = title,
theme = "peach",
examples = [[text_]]).launch()

import torch
from datasets import load_dataset
from console_logging.console import Console
import numpy as np
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from transformers import TrainingArguments, Trainer
from sklearn.metrics import f1_score, roc_auc_score, accuracy_score
from transformers import EvalPrediction
import torch
console = Console()

dataset = load_dataset("zeroshot/twitter-financial-news-sentiment", )


model = AutoModelForSequenceClassification.from_pretrained("bert-base-uncased")
tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")

#labels = [label for label in dataset['train'].features.keys() if label not in ['text']]

labels = ["Bearish", "Bullish", "Neutral"]

def preprocess_data(examples):
  # take a batch of texts
  text = examples["text"]
  # encode them
  encoding = tokenizer(text, padding="max_length", truncation=True, max_length=128)
  # add labels
  #labels_batch = {k: examples[k] for k in examples.keys() if k in labels}
  labels_batch = {'Bearish': [], 'Bullish': [], 'Neutral': []}
  for i in range (len(examples['label'])):
    labels_batch["Bearish"].append(False)
    labels_batch["Bullish"].append(False)
    labels_batch["Neutral"].append(False)
    
    if examples['label'][i] == 0:
      labels_batch["Bearish"][i] = True

    elif examples['label'][i] == 1:
      labels_batch["Bullish"][i] = True

    else:
      labels_batch["Neutral"][i] = True

  # create numpy array of shape (batch_size, num_labels)
  labels_matrix = np.zeros((len(text), len(labels)))
  # fill numpy array
  for idx, label in enumerate(labels):
    labels_matrix[:, idx] = labels_batch[label]

  encoding["labels"] = labels_matrix.tolist()
  
  return encoding

encoded_dataset = dataset.map(preprocess_data, batched=True, remove_columns=dataset['train'].column_names)

encoded_dataset.set_format("torch")

id2label = {idx:label for idx, label in enumerate(labels)}
label2id = {label:idx for idx, label in enumerate(labels)}

model = AutoModelForSequenceClassification.from_pretrained("bert-base-uncased",
                                                           problem_type="multi_label_classification", 
                                                           num_labels=len(labels),
                                                           id2label=id2label,
                                                           label2id=label2id)

batch_size = 8
metric_name = "f1"

args = TrainingArguments(
    f"bert-finetuned-sem_eval-english",
    evaluation_strategy = "epoch",
    save_strategy = "epoch",
    learning_rate=2e-5,
    per_device_train_batch_size=batch_size,
    per_device_eval_batch_size=batch_size,
    num_train_epochs=5,
    weight_decay=0.01,
    load_best_model_at_end=True,
    metric_for_best_model=metric_name,
    #push_to_hub=True,
)

# source: https://jesusleal.io/2021/04/21/Longformer-multilabel-classification/
def multi_label_metrics(predictions, labels, threshold=0.5):
    # first, apply sigmoid on predictions which are of shape (batch_size, num_labels)
    sigmoid = torch.nn.Sigmoid()
    probs = sigmoid(torch.Tensor(predictions))
    # next, use threshold to turn them into integer predictions
    y_pred = np.zeros(probs.shape)
    y_pred[np.where(probs >= threshold)] = 1
    # finally, compute metrics
    y_true = labels
    f1_micro_average = f1_score(y_true=y_true, y_pred=y_pred, average='micro')
    roc_auc = roc_auc_score(y_true, y_pred, average = 'micro')
    accuracy = accuracy_score(y_true, y_pred)
    # return as dictionary
    metrics = {'f1': f1_micro_average,
               'roc_auc': roc_auc,
               'accuracy': accuracy}
    return metrics

def compute_metrics(p: EvalPrediction):
    preds = p.predictions[0] if isinstance(p.predictions, 
            tuple) else p.predictions
    result = multi_label_metrics(
        predictions=preds, 
        labels=p.label_ids)
    return result


trainer = Trainer(
    model,
    args,
    train_dataset=encoded_dataset["train"],
    eval_dataset=encoded_dataset["validation"],
    tokenizer=tokenizer,
    compute_metrics=compute_metrics
)

trainer.train()

trainer.evaluate()