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--- |
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tags: |
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- generated_from_keras_callback |
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model-index: |
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- name: GeoBERT |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information Keras had access to. You should |
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probably proofread and complete it, then remove this comment. --> |
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# GeoBERT |
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GeoBERT is a NER model that was fine-tuned from SciBERT on the Geoscientific Corpus dataset. |
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The model was trained on the Labeled Geoscientific Corpus dataset (~1 million sentences). |
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## Intended uses |
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The NER product in this model has a goal to identify four main semantic types or categories related to Geosciences. |
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1. GeoPetro for any entities that belong to all terms in Geosciences |
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2. GeoMeth for all tools or methods associated with Geosciences |
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3. GeoLoc to identify geological locations |
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4. GeoTime for identifying the geological time scale entities |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- 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} |
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- training_precision: mixed_float16 |
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### Framework versions |
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- Transformers 4.22.1 |
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- TensorFlow 2.10.0 |
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- Datasets 2.4.0 |
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- Tokenizers 0.12.1 |
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## Model performances (metric: seqeval) |
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entity|precision|recall|f1 |
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-|-|-|- |
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GeoLoc |0.9727|0.9591|0.9658 |
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GeoMeth |0.9433|0.9447|0.9445 |
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GeoPetro|0.9767|0.9745|0.9756 |
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GeoTime |0.9695|0.9666|0.9680 |
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## How to use GeoBERT with HuggingFace |
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##### Load GeoBERT and its sub-word tokenizer : |
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```python |
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from transformers import AutoTokenizer, AutoModelForTokenClassification |
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tokenizer = AutoTokenizer.from_pretrained("botryan96/GeoBERT") |
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model = AutoModelForTokenClassification.from_pretrained("botryan96/GeoBERT") |
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#Define the pipeline |
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from transformers import pipeline |
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ner_machine = pipeline('ner',model = models,tokenizer=tokenizer, aggregation_strategy="simple") |
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#Define the sentence |
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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' |
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#Deploy the NER Machine |
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ner_machine(sentence) |
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``` |
