Update README.md

README.md

@@ -11,14 +11,26 @@ metrics:
 model-index:
 - name: iati-climate-multi-classifier-weighted2
   results: []
+datasets:
+- alex-miller/iati-policy-markers
+language:
+- en
+- fr
+- es
+- de
+pipeline_tag: text-classification
+widget:
+  - text: "VCA WWF Bolivia The programme will focus on women, young people and indigenous population living in the transboundary Pantanal - Chaco ecoregions (PACHA - Paraguay and Bolivia). Its objective is to “amplify their voices”, to ensure that they are participating, heard and taken into account in designing solutions for climate transition and common agendas to reach climate justice."
+    example_title: "Positive"
+  - text: "HIV/AIDS prevention by education and awareness raising with emphasis on gender issues/El Salvador"
+    example_title: "Negative"
 ---
 
-<!-- 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. -->
 
 # iati-climate-multi-classifier-weighted2
 
-This model is a fine-tuned version of [alex-miller/ODABert](https://huggingface.co/alex-miller/ODABert) on the None dataset.
+This model is a fine-tuned version of [alex-miller/ODABert](https://huggingface.co/alex-miller/ODABert) on a subset of the [alex-miller/iati-policy-markers](https://huggingface.co/datasets/alex-miller/iati-policy-markers) dataset.
+
 It achieves the following results on the evaluation set:
 - Loss: 0.7080
 - Accuracy: 0.8541
@@ -28,17 +40,11 @@ It achieves the following results on the evaluation set:
 
 ## Model description
 
-More information needed
+This model has been trained to identify both significant and principal climate mitigation and climate adaptation project titles and/or descriptions.
 
 ## Intended uses & limitations
 
-More information needed
-
-## Training and evaluation data
-
-More information needed
-
-## Training procedure
+As many of the donors in the training dataset have mixed up Adaptation and Mitigation, the model's ability to differentiate the two isn't perfect. But the sigmoid of the model logits do bias toward the correct class.
 
 ### Training hyperparameters
 
@@ -82,4 +88,4 @@ The following hyperparameters were used during training:
 - Transformers 4.41.0
 - Pytorch 2.0.1
 - Datasets 2.19.1
-- Tokenizers 0.19.1
+- Tokenizers 0.19.1