Update README.md

README.md

@@ -16,6 +16,16 @@ tags:
 This is a model card for detecting claims from an abstract of social science publications.
 The model takes an abstract, performs sentence tokenization, and predict a claim probability of each sentence.
 This model card is released by training on a [SCORE](https://www.cos.io/score) dataset.
+It achieves the following results on the test set:
+
+- Accuracy: 0.931597
+- Precision: 0.764563
+- Recall: 0.722477
+- F1: 0.742925
+
+
+## Model Usage
+You can access the model with huggingface's `transformers` as follows:
 
 ```py
 import spacy
@@ -54,4 +64,30 @@ def inference(abstract: str):
 claims = inference(abstract)  # string of claim joining with \n
 ```
 
+## Training procedure
+
+### Training Hyperparameters
+
+The following hyperparameters were used during training:
+
+learning_rate: 3e-05
+train_batch_size: 32
+eval_batch_size: 32
+n_epochs: 6
+
+### Training results
+
+0.038000	0.007086	0.997964	0.993499	0.995656	0.991350
+
+| Training Loss | Step | Validation Loss | Accuracy |    F1    | Precision |  Recall  |
+|:-------------:|:----:|:---------------:|:--------:|:--------:|:---------:|:--------:|
+| 0.038000      | 3996 | 0.007086        | 0.997964 | 0.993499 | 0.995656  | 0.991350 |
+
+### Framework versions
+- transformers 4.28.0
+- sentence-transformers 2.2.2
+- accelerate 0.19.0
+- datasets 2.12.0
+- spacy 3.5.3
+
 See more on `gradio` application in `biodatlab` space.