|
--- |
|
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) |
|
``` |