agriculture bert uncased

Introduction

Agriculture-BERT-Uncased is a BERT-based language model specifically designed for the agricultural domain. It is built on top of the SciBERT model and is trained using a dataset that includes scientific and general agricultural literature. The model is particularly suitable for language processing tasks related to agriculture.

Architecture

The model employs the BERT architecture, which uses a masked language modeling (MLM) technique. This involves randomly masking 15% of input words and training the model to predict these masked words, enabling it to learn a bidirectional representation of the sentence. This approach allows the model to understand context more effectively than traditional recurrent neural networks.

Training

Agriculture-BERT-Uncased was trained using a corpus comprising 1.2 million paragraphs from the National Agricultural Library (NAL) and 5.3 million paragraphs from various agricultural books and literature. The training utilized a self-supervised learning approach focused on masked language modeling (MLM), which is effective for domain-specific language models.

Guide: Running Locally

To run the Agriculture-BERT-Uncased model locally, follow these steps:

1. Install the Transformers library: Ensure you have the transformers library installed in your environment.

   pip install transformers
   

2. Load the model using the pipeline:

   from transformers import pipeline
   fill_mask = pipeline(
       "fill-mask",
       model="recobo/agriculture-bert-uncased",
       tokenizer="recobo/agriculture-bert-uncased"
   )
   fill_mask("[MASK] is the practice of cultivating plants and livestock.")
   

3. Run the model: The above code snippet demonstrates how to use the model for a fill-mask task.

For efficient performance, especially with large datasets, consider using cloud GPUs from providers like AWS, Google Cloud, or Azure.

License

The license terms for the Agriculture-BERT-Uncased model were not specified in the provided documentation. Users should refer to the Hugging Face page of the model for specific licensing details.