Update README.md

README.md

@@ -40,7 +40,7 @@ datasets:
 
 # Model description
 This `bert-causation-rating-dr1` model is a fine-tuned [biobert-base-cased-v1.2](https://huggingface.co/dmis-lab/biobert-base-cased-v1.2) model on a small set of manually annotated texts with causation labels. This model is tasked with classifying a sentence into different levels of strength of causation expressed in this sentence.
-
+Before tuning on this dataset, the `biobert-base-cased-v1.2` model is fine-tuned on a dataset containing causation labels from a published paper. This model starts from pre-trained [`kelingwang/bert-causation-rating-pubmed`](https://huggingface.co/kelingwang/bert-causation-rating-pubmed). For more information please view the link and my [GitHub page](https://github.com/Keling-Wang/causation_rating).
 The sentences in the dataset were rated independently by two researchers. This `dr1` version is tuned on the set of sentences with labels rated by Rater 1. 
 
 # Intended use and limitations
@@ -56,7 +56,7 @@ This model predicts strength of causation (SoC) labels based on the text inputs
 
 # Performance and hyperparameters
 ## Test metrics
-This model achieves the following results on the test dataset. The test dataset is a 25% held-out split of the entire dataset with `SEED=114514`.
+This model achieves the following results on the test dataset. The test dataset is a 25% held-out stratified split of the entire dataset with `SEED=114514`.
  * Loss: 0.5916
  * Off-by-1 accuracy: 88.1356
  * Off-by-2 accuracy: 100.0000
@@ -78,6 +78,7 @@ During the Bayesian optimization procedure for hyperparameter tuning, this model
 # Training settings
 
 The following training configurations apply:
+ * Pre-trained model: `kelingwang/bert-causation-rating-pubmed`
  * `seed`: 114514
  * `batch_size`: 128
  * `epoch`: 8