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botryan96/
GeoBERT_NER

Name: GeoBERT_NER
Rating: 2.5 (30 reviews)

transformers

bert

token-classification

generated_from_keras_callback

autotrain_compatible

endpoints_compatible

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GeoBERT_NER

GeoBERT_NER is a NER model that was fine-tuned from SciBERT on the Geoscientific Corpus dataset. The model was trained on the Labeled Geoscientific Corpus dataset (~1 million sentences).

Intended uses

The NER product in this model has a goal to identify four main semantic types or categories related to Geosciences.

GeoPetro for any entities that belong to all terms in Geosciences
GeoMeth for all tools or methods associated with Geosciences
GeoLoc to identify geological locations
GeoTime for identifying the geological time scale entities

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
GeoLoc	0.9727	0.9591	0.9658
GeoMeth	0.9433	0.9447	0.9445
GeoPetro	0.9767	0.9745	0.9756
GeoTime	0.9695	0.9666	0.9680

How to use GeoBERT_NER with HuggingFace

Load GeoBERT and its sub-word tokenizer :

python

from transformers import AutoTokenizer, AutoModelForTokenClassification

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

#Define the pipeline
from transformers import pipeline
ner_machine = pipeline('ner',model = models,tokenizer=tokenizer, aggregation_strategy="simple")

#Define the sentence
sentence = 'In North America, the water storage in the seepage face model is higher than the base case because positive pore pressure is requisite for drainage through a seepage face boundary condition. The result from the resistivity data supports the notion, especially in the northern part of the Sandstone Sediment formation. The active formation of America has a big potential for Oil and Gas based on the seismic section, has been activated since the Paleozoic'

#Deploy the NER Machine
ner_machine(sentence)