---
language: english
tags:
- sentiment-analysis
- distilbert
---

# Sentiment Analysis with DistilBert

This model is a fine-tuned version of DistilBert for sentiment analysis on the IMDB dataset.

## How the model was trained

This model was trained using the Hugging Face Transformers library. It was trained for 1 epoch on the IMDB dataset, with a batch size of 4 and a learning rate of 5e-5.

## How to use

You can use this model for sentiment analysis. Here's an example of how to do this in Python:

```python
from transformers import pipeline

# Load the model
classifier = pipeline('sentiment-analysis', model='your-model-name')

# Classify some text
result = classifier("I love this movie!")[0]
print(f"label: {result['label']}, with score: {result['score']:.4f}")

{"license": "openrail"}
license:openrail region:us
TAGS
#license-openrail #region-us 

---
language: english
tags:
- sentiment-analysis
- distilbert
---

# Sentiment Analysis with DistilBert

This model is a fine-tuned version of DistilBert for sentiment analysis on the IMDB dataset.

## How the model was trained

This model was trained using the Hugging Face Transformers library. It was trained for 1 epoch on the IMDB dataset, with a batch size of 4 and a learning rate of 5e-5.

## How to use

You can use this model for sentiment analysis. Here's an example of how to do this in Python:

'''python
from transformers import pipeline

# Load the model
classifier = pipeline('sentiment-analysis', model='your-model-name')

# Classify some text
result = classifier("I love this movie!")[0]
print(f"label: {result['label']}, with score: {result['score']:.4f}")

# Sentiment Analysis with DistilBert

This model is a fine-tuned version of DistilBert for sentiment analysis on the IMDB dataset. ## How the model was trained

This model was trained using the Hugging Face Transformers library. It was trained for 1 epoch on the IMDB dataset, with a batch size of 4 and a learning rate of 5e-5. ## How to use

You can use this model for sentiment analysis. Here's an example of how to do this in Python:

'''python
from transformers import pipeline # Load the model
classifier = pipeline('sentiment-analysis', model='your-model-name') # Classify some text
result = classifier("I love this movie!")[0]
print(f"label: {result['label']}, with score: {result['score']:.4f}")
TAGS
#license-openrail #region-us 
 # Sentiment Analysis with DistilBert

This model is a fine-tuned version of DistilBert for sentiment analysis on the IMDB dataset. ## How the model was trained

This model was trained using the Hugging Face Transformers library. It was trained for 1 epoch on the IMDB dataset, with a batch size of 4 and a learning rate of 5e-5. ## How to use

You can use this model for sentiment analysis. Here's an example of how to do this in Python:

'''python
from transformers import pipeline # Load the model
classifier = pipeline('sentiment-analysis', model='your-model-name') # Classify some text
result = classifier("I love this movie!")[0]
print(f"label: {result['label']}, with score: {result['score']:.4f}")