You can deploy this model and start earning money today!

raicrits/
DistilFEVERen

Name: DistilFEVERen
Availability: InStock

transformers

distilbert

text-classification

generated_from_keras_callback

distilbert/distilbert-base-multilingual-cased

Views

No views yet

DistilFEVERen

This model is a fine-tuned version of distilbert-base-multilingual-cased, specifically trained on the Recognize Textual Entailment (RTE) task using the first fold split of FEVER dataset in English. RTE focuses on evaluating the support or refutation of claims within a given text. The labels used for classification are as follows:

0: SUPPORT (indicating that the claim is supported by the text)
1: CONFUTE (indicating that the claim is refuted by the text)
2: NOT ENOUGH INFO (indicating that there is insufficient information in the text to support or refute the claim).

Inference API Usage

When using the Inference API, it is important to note that the input should be provided by pasting the text first, followed by the claim, without any spaces or separators. The model's tokenizer concatenates these inputs in the specified order. Interestingly, inverting the order of pasting (claim first, then text) seems to produce similar results, suggesting that the model generally captures coherence within a given text (the label 0 indicates a coherent text, while the other label 1 signify an incoherent text).

Training procedure

The model was trained on Kaggle using as accelerator a GPU T4 x2. See the complete notebook here:

python

import json
import numpy as np
import os
import pickle
from IPython.display import clear_output
import pandas as pd
import tensorflow as tf
import transformers
from datasets import load_dataset
from sklearn.metrics import confusion_matrix, classification_report
from sklearn.model_selection import train_test_split
from transformers import DistilBertTokenizer, TFDistilBertForSequenceClassification
import warnings

# Silence all warnings
warnings.filterwarnings("ignore")


# Try to create a directory named "models"
try:
    os.makedirs("models")
except:
    # If the directory already exists or if there's an error, do nothing (pass)
    pass

# Try to create a directory named "results"
try:
    os.makedirs("results")
except:
    # If the directory already exists or if there's an error, do nothing (pass)
    pass

# Try to create a directory named "history"
try:
    os.makedirs("history")
except:
    # If the directory already exists or if there's an error, do nothing (pass)
    pass


# Flag to determine if existing models and histories should be overwritten
overwrite = True

# Load dataset for the first fold
data = load_dataset("raicrits/fever_folds", data_files="folds_en/1.json")['train']
test = data['test'][0]
val_set = data['val'][0]
train_set = data['train'][0]

# Define paths for model, results, and history
model_path = 'models/DistilFEVERen_weights_0.h5'
results_path = "results/DistilFEVERen_0.json"
history_path = 'history/DistilFEVERen_0.pickle'

# Load the tokenizer
tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-multilingual-cased')

# Preprocess the data
test_encodings = tokenizer(test['text'], test['claim'], truncation=True, padding=True, max_length=256, return_tensors='tf')
test_labels = tf.convert_to_tensor(test['label'])

train_encodings = tokenizer(train_set['text'], train_set['claim'], truncation=True, padding=True, return_tensors='tf')
val_encodings = tokenizer(val_set['text'], val_set['claim'], truncation=True, padding=True, return_tensors='tf')

train_labels = tf.convert_to_tensor(train_set['label'])
val_labels = tf.convert_to_tensor(val_set['label'])

# Check if the model and history already exist for the first fold
if not overwrite and os.path.exists(model_path):
    print("Model and history already exist for fold {}. Loading...".format(0))
    model = TFDistilBertForSequenceClassification.from_pretrained('distilbert-base-multilingual-cased', num_labels=3)
    model.load_weights(model_path)
    # with open(history_path, 'rb') as file_pi:
    #    history = pickle.load(file_pi)
else:
    # Create a new model and define loss, optimizer, and callbacks
    model = TFDistilBertForSequenceClassification.from_pretrained('distilbert-base-multilingual-cased', num_labels=3)
    loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
    optimizer = tf.keras.optimizers.Adam(learning_rate=5e-5)
    model.compile(optimizer=optimizer, loss=loss, metrics=['accuracy'])
    model_checkpoint = tf.keras.callbacks.ModelCheckpoint(
        model_path,
        monitor='val_loss',
        save_best_only=True,
        mode='min',
        save_weights_only=True
    )
    early_stopping = tf.keras.callbacks.EarlyStopping(
        monitor='val_loss',
        patience=1,
        mode='min',
        restore_best_weights=True
    )

    # Train the model for the first fold
    clear_output(wait=True)
    history = model.fit(
        [train_encodings['input_ids'], train_encodings['attention_mask']], train_labels,
        validation_data=([val_encodings['input_ids'], val_encodings['attention_mask']], val_labels),
        batch_size=10,
        epochs=100,
        callbacks=[early_stopping, model_checkpoint]
    )

    # Save the training history
    with open(history_path, 'wb') as file_pi:
        pickle.dump(history.history, file_pi)

Inference procedure

python

def getPrediction(model,tokenizer,claim,text):
    encodings = tokenizer([text], [claim], truncation=True, padding=True, max_length=256, return_tensors='tf')
    preds = model.predict([encodings['input_ids'], encodings["attention_mask"]])
    return preds

text = "Soul Food is a 1997 American comedy-drama film produced by Kenneth `` Babyface '' Edmonds , Tracey Edmonds and Robert Teitel and released by Fox 2000 Pictures ."
claim = 'Fox 2000 Pictures released the film Soul Food .'
getPrediction(model,tokenizer,claim,text)

Evaluation results

It achieves the following results on the evaluation set:

Framework versions

Transformers 4.35.0
TensorFlow 2.13.0
Datasets 2.1.0
Tokenizers 0.14.1
Numpy 1.24.3

raicrits/DistilFEVERen