Distilbert Eco ZeroShot

🚀 disitilbert_zeroshot_economics

This model is a fine - tuned version of [distilbert - base - uncased](https://huggingface.co/distilbert - base - uncased) on an unknown dataset, specialized for zero - shot classification in the field of economics.

🚀 Quick Start

This model is designed for zero - shot classification tasks in the economic domain. It's a fine - tuned version of distilbert - base - uncased and can be used for various economic text classification scenarios.

✨ Features

• Fine - tuned on the [teknium/dataforge - economics](https://huggingface.co/datasets/teknium/dataforge - economics) dataset for zero - shot classification.
• Utilizes specific hyperparameters during training to optimize performance.

📦 Installation

No specific installation steps are provided in the original README. If you want to use this model, you may need to install relevant libraries such as transformers, pytorch, datasets, and tokenizers according to the framework versions mentioned. For example:

pip install transformers==4.36.0 torch==2.0.0 datasets==2.16.1 tokenizers==0.15.0

📚 Documentation

Training and evaluation data

The model was trained on the [teknium/dataforge - economics](https://huggingface.co/datasets/teknium/dataforge - economics) dataset for the zero - shot classification task.

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 1e - 05
• train_batch_size: 32
• eval_batch_size: 32
• seed: 42
• optimizer: Adam with betas=(0.9, 0.999) and epsilon = 1e - 08
• lr_scheduler_type: linear
• num_epochs: 5

Training results

The evaluation loss is 0.0503.

Framework versions

• Transformers 4.36.0
• Pytorch 2.0.0
• Datasets 2.16.1
• Tokenizers 0.15.0

📄 License

This model is released under the Apache 2.0 license.