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---
tags:
- generated_from_keras_callback
model-index:
- name: GeoBERT
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# GeoBERT
GeoBERT 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.
1. GeoPetro for any entities that belong to all terms in Geosciences
2. GeoMeth for all tools or methods associated with Geosciences
3. GeoLoc to identify geological locations
4. 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 with HuggingFace
##### Load GeoBERT and its sub-word tokenizer :
```python
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("botryan96/GeoBERT")
model = AutoModelForTokenClassification.from_pretrained("botryan96/GeoBERT")
``` |