Agriculture-BERT-Uncased Open-Source Model in the Agricultural Domain - Optimizing Agricultural Text Processing Capabilities

Agriculture Bert Uncased

Developed by recobo

A domain-adapted pre-trained model for agriculture based on the SciBERT checkpoint, optimized for processing agricultural texts.

Large Language Model

Transformers

English#Agricultural Text Understanding #Scientific Literature Adaptation #Bidirectional Semantic Modeling

Downloads 483

Release Time : 3/2/2022

Model Overview

This model is specifically optimized for the agricultural domain, trained using self-supervised masked language modeling (MLM), effectively handling agricultural scientific literature and practical texts.

Model Features

Agricultural Domain Optimization

Specifically optimized for agricultural texts, enhancing understanding of agricultural terms and concepts.

Bidirectional Semantic Representation

Utilizes BERT architecture to support bidirectional semantic representation of sentences, outperforming traditional RNN word-by-word processing.

Large-Scale Training Data

Trained on 6.5 million paragraphs from the National Agricultural Library and agricultural books, covering a wide range of agricultural knowledge.

Model Capabilities

Agricultural Text Understanding

Agricultural Term Prediction

Agricultural Domain Semantic Analysis

Use Cases

Agricultural Research

Agricultural Literature Analysis

Assists researchers in understanding and analyzing professional terms and concepts in agricultural scientific literature.

Agricultural Knowledge Extraction

Extracts key information and knowledge from agricultural texts.

Agricultural Practice

Agricultural Technical Document Processing

Processes technical documents and guidance materials related to agricultural practices.

🚀 BERT for Agriculture Domain

A BERT - based language model tailored for the agriculture domain, offering enhanced performance in agricultural text processing.

🚀 Quick Start

You can use the following code to utilize the fill - mask function of this model:

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.")

✨ Features

This is a BERT - based language model that undergoes further pre - training from the checkpoint of SciBERT.
The dataset is a well - balanced collection of scientific and general works in the agriculture domain, which encompasses knowledge from different areas of agricultural research and practical knowledge.
The corpus consists of 1.2 million paragraphs from the National Agricultural Library (NAL) of the US Gov and 5.3 million paragraphs from books and common literature in the Agriculture Domain.
It uses the self - supervised learning approach of Masked Language Modeling (MLM) for training. In MLM, the model randomly masks 15% of the words in the input sentence and then predicts the masked words. This enables the model to learn a bidirectional representation of the sentence, different from traditional recurrent neural networks (RNNs) and autoregressive models like GPT.

💻 Usage Examples

Basic Usage

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.")

```markdown
| Property | Details |
|----------|---------|
| Model Type | A BERT - based language model further pre - trained from SciBERT |
| Training Data | 1.2 million paragraphs from National Agricultural Library (NAL) of the US Gov and 5.3 million paragraphs from books and common literature in the Agriculture Domain |

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