---
license: apache-2.0
base_model: distilbert-base-uncased-finetuned-sst-2-english
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: LLM_project
  results: []
---

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# LLM_project

This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on IMDb reviews dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0852
- Accuracy: 0.9804

## Model description

This model is a fine-tuned version of the DistilBERT model, which is a smaller, faster, and lighter version of BERT (Bidirectional Encoder Representations from Transformers). The base model has been pre-trained on a large corpus of English data in a self-supervised fashion, and fine-tuning was performed using a sentiment analysis dataset. The model is uncased, meaning it does not distinguish between uppercase and lowercase letters.

DistilBERT retains 97% of BERT's language understanding while being 60% faster and 40% smaller, making it highly efficient for various NLP tasks including sentiment analysis, which this model is specifically tuned for.

## Intended uses & limitations

**Intended Uses:**

> Sentiment analysis of English text, particularly for binary classification tasks such as identifying positive and negative sentiments.
Can be applied to product reviews, social media posts, customer feedback, etc.

**Limitations:**

> The model's performance is highly dependent on the quality and representativeness of the fine-tuning dataset.
May not perform well on text data that is very different from the fine-tuning dataset.
Limited by the scope of sentiment analysis and may not capture nuanced sentiments or complex emotions.
Not suitable for tasks outside binary sentiment classification without further fine-tuning.

## Training and evaluation data

The model was evaluated on a separate validation set that was not seen during training. This evaluation set is also designed for sentiment analysis and includes examples that reflect real-world use cases.

## Training procedure
### Procedure
1. Data Preprocessing: Text data was tokenized using the DistilBERT tokenizer, which converts text into a format suitable for the model.
2. Model Fine-Tuning: The pre-trained DistilBERT model was fine-tuned on the training dataset. Fine-tuning involves adjusting the weights of the model to better fit the sentiment analysis task.
3. Evaluation: After training, the model was evaluated on the validation set to measure its performance in terms of loss and accuracy.

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0743        | 1.0   | 1250 | 0.1208          | 0.9696   |
| 0.145         | 2.0   | 2500 | 0.0852          | 0.9804   |
| 0.0322        | 3.0   | 3750 | 0.1043          | 0.9822   |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.3.1+cpu
- Datasets 2.20.0
- Tokenizers 0.19.1