---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- imdb
model-index:
- name: distilbert-base-uncased-finetuned-imdb
  results: []
language:
- en
metrics:
- perplexity
---

# distilbert-base-uncased-finetuned-imdb-v2

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 2.3033

## Model description

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

```