Update README.md

README.md

@@ -11,25 +11,19 @@ probably proofread and complete it, then remove this comment. -->
 
 # GeoBERT
 
-This model was trained from scratch on an unknown dataset.
-It achieves the following results on the evaluation set:
-- Train Loss: 0.0660
-- Validation Loss: 0.0470
-- Epoch: 0
+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).
 
-## Model description
 
-More information needed
+## Intended uses
 
-## Intended uses & limitations
+The NER product in this model has a goal to identify four main semantic types or categories related to Geosciences. 
 
-More information needed
+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 and evaluation data
-
-More information needed
-
-## Training procedure
 
 ### Training hyperparameters
 
@@ -37,12 +31,6 @@ 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
 
-### Training results
-
-| Train Loss | Validation Loss | Epoch |
-|:----------:|:---------------:|:-----:|
-| 0.0660     | 0.0470          | 0     |
-
 
 ### Framework versions
 
@@ -50,3 +38,30 @@ The following hyperparameters were used during training:
 - TensorFlow 2.10.0
 - Datasets 2.4.0
 - Tokenizers 0.12.1
+
+## Model performances (metric: seqeval)
+
+Overall
+
+precision|recall|f1
+-|-|-
+0.8859|0.8971|0.8914
+
+By entity
+
+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")