---
license: cc-by-nc-4.0
datasets:
- mediabiasgroup/BABE
language:
- en
base_model:
- FacebookAI/roberta-base
pipeline_tag: text-classification
---
Here’s a template for a `README.md` file that you can reuse for each of your models on Hugging Face. It is designed to provide a comprehensive overview of the model, its usage, links to relevant papers, datasets, and results:

---

# Model Name

**Model Name:** `Your Model Name`  
**Model Type:** Token-level / Sentence-level / Paragraph-level Classifier  
**Organization:** [Your Lab's Name or Organization](https://huggingface.co/your_org)  
**Model Version:** `v1.0.0`  
**Framework:** `PyTorch` or `TensorFlow`  
**License:** `MIT / Apache 2.0 / Other`

---

## Model Overview

This model is a [token-level/sentence-level/paragraph-level] classifier that was trained for [specific task, e.g., sentiment analysis, named entity recognition, etc.]. The model is based on [model architecture, e.g., BERT, RoBERTa, etc.] and has been fine-tuned on [mention the dataset] for [number of epochs or other training details].

It achieves state-of-the-art performance on [mention dataset or task] and is specifically designed for [specific domain or industry, if applicable].

---

## Training details

- **Base Model:** [mention architecture, e.g., BERT-base, RoBERTa-large, etc.]  
- **Number of Parameters:** [number of parameters]  
- **Max Sequence Length:** [max input length, if relevant]

### Training Data

The model was fine-tuned on the [name of dataset] dataset. This dataset consists of [short description of dataset, e.g., number of instances, labels, any important data characteristics].

You can find the dataset [here](dataset_url).

---

## Evaluation Results

The model was evaluated on [name of dataset] and achieved the following results:

- **Accuracy:** [accuracy score]  
- **F1-Score:** [F1 score]  
- **Precision:** [precision score]  
- **Recall:** [recall score]  

For detailed evaluation results, see the corresponding paper or evaluation logs.

---

## Usage

To use this model in your code, install the required libraries:

```bash
pip install transformers
```

Then, load the model as follows:

```python
from transformers import AutoModelForSequenceClassification, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("your_org/your_model")
model = AutoModelForSequenceClassification.from_pretrained("your_org/your_model")

# Example input
input_text = "Your example sentence goes here."
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model(**inputs)

# Accessing the predicted class
predicted_class = outputs.logits.argmax(dim=-1)
print(f"Predicted class: {predicted_class}")
```

---

## Example Code

Here’s an example for batch classification:

```python
import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained("your_org/your_model")
model = AutoModelForSequenceClassification.from_pretrained("your_org/your_model")

# Example sentences
sentences = ["Sentence 1", "Sentence 2", "Sentence 3"]
inputs = tokenizer(sentences, padding=True, truncation=True, return_tensors="pt")

with torch.no_grad():
    outputs = model(**inputs)

predicted_classes = outputs.logits.argmax(dim=-1)
print(f"Predicted classes: {predicted_classes}")
```

---

## Related Papers

This model is described in the following paper(s):

- **Title:** [Paper Title](paper_url)  
  **Authors:** [Author Names]  
  **Conference/Journal:** [Conference/Journal Name]  
  **Year:** [Year]

Please cite this paper if you use the model.

---


## Limitations

- The model is limited to [token-level/sentence-level/paragraph-level] classification tasks.  
- Performance may degrade on out-of-domain data.
- [Other known limitations, e.g., bias in data, challenges with specific languages.]

---

## Citation

If you use this model, please cite the following paper(s):

```bibtex
@article{your_citation,
  title={Your Title},
  author={Your Name and Co-authors},
  journal={Journal Name},
  year={Year},
  publisher={Publisher},
  url={paper_url}
}
```

---

Feel free to adapt this template to match the specific needs of each model. Let me know if you'd like to adjust any sections further!