Text Classification
Transformers
TensorFlow
bert
generated_from_keras_callback
text-embeddings-inference
Instructions to use botryan96/GeoBERT_analyzer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use botryan96/GeoBERT_analyzer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="botryan96/GeoBERT_analyzer")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("botryan96/GeoBERT_analyzer") model = AutoModelForSequenceClassification.from_pretrained("botryan96/GeoBERT_analyzer", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| 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) | |
| ``` |