readme: ---
  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_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)
  ```