--- tags: - generated_from_keras_callback model-index: - name: GeoBERT results: [] --- # 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 : ```python 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) ```