metadata

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
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.

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:

pip install transformers

Then, load the model as follows:

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:

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}")

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):

@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!

mediabiasgroup
/

da-roberta-pt