Update README.md

README.md

@@ -12,14 +12,15 @@ metrics:
 model-index:
 - name: BiomedBERT-AC-LF-Classification
   results: []
+datasets:
+- surrey-nlp/PLOD-CW-25
+language:
+- en
 ---
 
-<!-- This model card has been generated automatically according to the information the Trainer had access to. You
-should probably proofread and complete it, then remove this comment. -->
-
 # BiomedBERT-AC-LF-Classification
 
-This model is a fine-tuned version of [microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext](https://huggingface.co/microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext) on an unknown dataset.
+This model is a fine-tuned version of [microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext](https://huggingface.co/microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext) on the PLOD-CW-25 dataset.
 It achieves the following results on the evaluation set:
 - Loss: 0.2703
 - Precision: 0.7821
@@ -27,9 +28,16 @@ It achieves the following results on the evaluation set:
 - F1: 0.8231
 - Accuracy: 0.9204
 
+It achieves the following results on the test set:
+- Loss: 0.1384
+- Precision: 0.8473
+- Recall: 0.9281
+- F1: 0.8858
+- Accuracy: 0.9529
+
 ## Model description
 
-More information needed
+This model is a fine-tuned model, designed to detect abbreviations and long forms in biomedical text. Abbreviations and long forms are tagged in the BIO format, with the following labels, B-AC, B-LF, I-LF and O.
 
 ## Intended uses & limitations
 
@@ -68,4 +76,4 @@ The following hyperparameters were used during training:
 - Transformers 4.52.4
 - Pytorch 2.6.0+cu124
 - Datasets 3.6.0
-- Tokenizers 0.21.1
+- Tokenizers 0.21.1