GeoBERT_Analyzer

GeoBERT_Analyzer is a Text Classification model that was fine-tuned from GeoBERT on the Geoscientific Corpus dataset. The model was trained on the Labeled Geoscientific & Non-Geosceintific Corpus dataset (21416 x 2 sentences).

Intended uses

The train aims to make the Language Model have the ability to distinguish between Geoscience and Non – Geoscience (General) corpus

Training hyperparameters

The following hyperparameters were used during training:

optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 14000, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
training_precision: mixed_float16

Framework versions

Transformers 4.22.1
TensorFlow 2.10.0
Datasets 2.4.0
Tokenizers 0.12.1

Model performances (metric: seqeval)

entity	precision	recall	f1
General	0.9976	0.9980	0.9978
Geoscience	0.9980	0.9984	0.9982

How to use GeoBERT with HuggingFace

Load GeoBERT and its sub-word tokenizer :

from transformers import AutoTokenizer, AutoModelForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained("botryan96/GeoBERT_analyzer")
model = AutoModelForTokenClassification.from_pretrained("botryan96/GeoBERT_analyzer")

#Define the pipeline
from transformers import pipeline
anlyze_machine=pipeline('text-classification',model = model_checkpoint2)

#Define the sentences
sentences = ['the average iron and sulfate concentrations were calculated to be 19 . 6 5 . 2 and 426 182 mg / l , respectively .',
            'She first gained media attention as a friend and stylist of Paris Hilton']

#Deploy the machine
anlyze_machine(sentences)