This model is a fine-tuned version of DistilBERT base uncased on the IMDb dataset. It was trained to predict the next word in a sentence using masked language modeling. The model has been fine-tuned to adapt to the language patterns and sentiment present in movie reviews.

Intended uses & limitations

This model is primarily designed for the fill-mask task, a type of language modeling where the model is trained to predict missing words within a given context. It excels at completing sentences or phrases by predicting the most likely missing word based on the surrounding text. This functionality makes it valuable for a wide range of natural language processing tasks, such as generating coherent text, improving auto-completion in writing applications, and enhancing conversational agents' responses. However, it may have limitations in handling domain-specific language or topics not present in the IMDb dataset. Additionally, it may not perform well on languages other than English.

Training and evaluation data

The model was trained on a subset of the IMDb dataset, containing 40,000 reviews for fine-tuning. The evaluation was conducted on a separate test set of 6,000 reviews.

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 2e-05
train_batch_size: 64
eval_batch_size: 64
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss
2.4912	1.0	625	2.3564
2.4209	2.0	1250	2.3311
2.4	3.0	1875	2.3038

Framework versions

Transformers 4.31.0
Pytorch 2.0.1+cu118
Datasets 2.14.4
Tokenizers 0.13.3

How to use

python

import torch
import pandas as pd
from transformers import AutoTokenizer, AutoModelForMaskedLM

# Load the tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("Francesco-A/distilbert-base-uncased-finetuned-imdb-v2")
model = AutoModelForMaskedLM.from_pretrained("Francesco-A/distilbert-base-uncased-finetuned-imdb-v2")

# Example sentence
sentence = "This movie is really [MASK]."

# Tokenize the sentence
inputs = tokenizer(sentence, return_tensors="pt")

# Get the model's predictions
with torch.no_grad():
    outputs = model(**inputs)

# Get the top-k predicted tokens and their probabilities
k = 5  # Number of top predictions to retrieve
masked_token_index = inputs["input_ids"].tolist()[0].index(tokenizer.mask_token_id)
predicted_token_logits = outputs.logits[0, masked_token_index]
topk_values, topk_indices = torch.topk(torch.softmax(predicted_token_logits, dim=-1), k)

# Convert top predicted token indices to words
predicted_tokens = [tokenizer.decode(idx.item()) for idx in topk_indices]
# Convert probabilities to Python floats
probs = topk_values.tolist()

# Create a DataFrame to display the top predicted words and probabilities
data = {
    "Predicted Words": predicted_tokens,
    "Probability": probs,
}

df = pd.DataFrame(data)

# Display the DataFrame
df