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metaextractor

Token Classification
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PyTorch
roberta
Beta
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metadata
tags:
  - Beta
license: mit
thumbnail: >-
  https://huggingface.co/finding-fossils/metaextractor/resolve/main/ffossils-logo-text.png
widget:
  - text: The core sample was aged at 12300 - 13500 BP and found at 210m a.s.l.
    example_title: Age/Alti
  - text: >-
      In Northern Canada, the BGC site core was primarily made up of Pinus
      pollen.
    example_title: Taxa/Site/Region

MetaExtractor

This model extracts metadata from research articles related to Paleoecology.

The entities detected by this model are:

  • AGE: when historical ages are mentioned such as 1234 AD or 4567 BP (before present)
  • TAXA: plant or animal taxa names indicating what samples contained
  • GEOG: geographic coordinates indicating where samples were excavated from, e.g. 12'34"N 34'23"W
  • SITE: site names for where samples were excavated from
  • REGION: more general regions to provide context for where sites are located
  • EMAIL: researcher emails in the articles able to be used for follow-up contact
  • ALTI: altitudes of sites from where samples were excavated, e.g. 123 m a.s.l (above sea level)

Model Details

Model Description

  • Developed by: Ty Andrews, Jenit Jain, Shaun Hutchinson, Kelly Wu, and Simon Goring
  • Shared by: Neotoma Paleocology Database
  • Model type: Token Classification
  • Language(s) (NLP): English
  • License: MIT
  • Finetuned from model: roberta-base

Model Sources [optional]

Uses

Direct Use

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Downstream Use [optional]

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Out-of-Scope Use

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Bias, Risks, and Limitations

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Recommendations

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

How to Get Started with the Model

Use the code below to get started with the model.

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Training Details

Training Data

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Training Procedure

Preprocessing [optional]

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Training Hyperparameters

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Speeds, Sizes, Times [optional]

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Evaluation

Testing Data, Factors & Metrics

Testing Data

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Factors

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Metrics

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Results

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Summary

Model Examination [optional]

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Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

  • Hardware Type: [More Information Needed]
  • Hours used: [More Information Needed]
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Technical Specifications [optional]

Model Architecture and Objective

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Compute Infrastructure

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Hardware

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Software

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Citation [optional]

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