Update README.md

README.md

@@ -6,6 +6,17 @@ tags:
 model-index:
 - name: TAPP-multilabel-bge
   results: []
+datasets:
+- GIZ/policy_classification
+co2_eq_emissions:
+  emissions: 71.4552917731392
+  source: codecarbon
+  training_type: fine-tuning
+  on_cloud: true
+  cpu_model: Intel(R) Xeon(R) CPU @ 2.30GHz
+  ram_total_size: 12.6747894287109
+  hours_used: 1.36
+  hardware_used: 1 x Tesla T4
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -13,9 +24,11 @@ should probably proofread and complete it, then remove this comment. -->
 
 # TAPP-multilabel-bge
 
-This model is a fine-tuned version of [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) on the None dataset.
+This model is a fine-tuned version of [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) on the [Policy-Classification](https://huggingface.co/datasets/GIZ/policy_classification) dataset.
+
+*The loss function BCEWithLogitsLoss is modified with pos_weight to focus on recall, therefore instead of loss the evaluation metrics are used to assess the model performance during training*
 It achieves the following results on the evaluation set:
-- Loss: 0.9217
+
 - Precision-micro: 0.7772
 - Precision-samples: 0.7644
 - Precision-weighted: 0.7756
@@ -28,7 +41,16 @@ It achieves the following results on the evaluation set:
 
 ## Model description
 
-More information needed
+The purpose of this model is to predict multiple labels simultaneously from a given input data. Specifically, the model will predict four labels - 
+ActionLabel, PlansLabel, PolicyLabel, and TargetLabel - that are relevant to a particular task or application
+- **Target**: Targets are an intention to achieve a specific result, for example, to reduce GHG emissions to a specific level 
+            (a GHG target) or increase energy efficiency or renewable energy to a specific level (a non-GHG target), typically by  
+            a certain date.
+- **Action**: Actions are an intention to implement specific means of achieving GHG reductions, usually in forms of concrete projects.
+- **Policies**: Policies are domestic planning documents such as policies, regulations or guidlines.
+- **Plans**:Plans  are broader than specific policies or actions, such as a general intention to ‘improve efficiency’, ‘develop renewable energy’, etc.
+
+*The terms come from the World Bank's NDC platform and WRI's publication*
 
 ## Intended uses & limitations
 
@@ -36,7 +58,21 @@ More information needed
 
 ## Training and evaluation data
 
-More information needed
+- Training Dataset: 10031
+| Class | Positive Count of Class|
+|:-------------|:--------|
+| Action | 5416 |
+| Plans | 2140 |
+| Policy | 1396|
+| Target | 2911 |
+
+- Validation Dataset: 932
+| Class | Positive Count of Class|
+|:-------------|:--------|
+| Action | 513 |
+| Plans | 198 |
+| Policy | 122 |
+| Target | 256 |
 
 ## Training procedure
 
@@ -64,10 +100,16 @@ The following hyperparameters were used during training:
 | 0.0291        | 6.0   | 3762 | 0.8849          | 0.7773          | 0.7640            | 0.7776             | 0.8301       | 0.7890         | 0.8301          | 0.8028   | 0.7597     | 0.8027      |
 | 0.0147        | 7.0   | 4389 | 0.9217          | 0.7772          | 0.7644            | 0.7756             | 0.8329       | 0.7920         | 0.8329          | 0.8041   | 0.7609     | 0.8029      |
 
+|label          | precision |recall |f1-score| support|
+|:-------------:|:---------:|:-----:|:------:|:------:|
+|Action	|0.826   	|0.883  |0.853   |	513.0  |
+|Plans	        |0.653	    |0.646  |0.649   |	198.0  |
+|Policy	|0.726      |0.803  |0.762   |	122.0  |
+|Target	    |0.791      |0.890  |0.838   |	256.0  |
 
 ### Framework versions
 
 - Transformers 4.38.1
 - Pytorch 2.1.0+cu121
 - Datasets 2.18.0
-- Tokenizers 0.15.2
+- Tokenizers 0.15.2