Update README.md

README.md

@@ -6,6 +6,17 @@ tags:
 model-index:
 - name: ADAPMIT-multilabel-climatebert
   results: []
+datasets:
+- GIZ/policy_classification
+co2_eq_emissions:
+  emissions: 23.3572576873636
+  source: codecarbon
+  training_type: fine-tuning
+  on_cloud: true
+  cpu_model: Intel(R) Xeon(R) CPU @ 2.00GHz
+  ram_total_size: 12.6747894287109
+  hours_used: 0.529
+  hardware_used: 1 x Tesla T4
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -13,7 +24,7 @@ should probably proofread and complete it, then remove this comment. -->
 
 # ADAPMIT-multilabel-climatebert
 
-This model is a fine-tuned version of [climatebert/distilroberta-base-climate-f](https://huggingface.co/climatebert/distilroberta-base-climate-f) on the None dataset.
+This model is a fine-tuned version of [climatebert/distilroberta-base-climate-f](https://huggingface.co/climatebert/distilroberta-base-climate-f) on the [Policy-Classification](https://huggingface.co/datasets/GIZ/policy_classification) dataset.
 It achieves the following results on the evaluation set:
 - Loss: 0.3535
 - Precision-micro: 0.8999
@@ -28,7 +39,8 @@ 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 2 labels - 
+AdaptationLabel, MitigationLabel - that are relevant to a particular task or application
 
 ## Intended uses & limitations
 
@@ -36,7 +48,21 @@ 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
 
@@ -62,10 +88,16 @@ The following hyperparameters were used during training:
 | 0.0767        | 4.0   | 3136 | 0.3367          | 0.8999          | 0.8563            | 0.9000             | 0.9173       | 0.8588         | 0.9173          | 0.9085   | 0.8524     | 0.9085      |
 | 0.0475        | 5.0   | 3920 | 0.3535          | 0.8999          | 0.8559            | 0.9001             | 0.9173       | 0.8592         | 0.9173          | 0.9085   | 0.8521     | 0.9085      |
 
+|label          | precision |recall |f1-score| support|
+|:-------------:|:---------:|:-----:|:------:|:------:|
+|Action	|0.828   	|0.807  |0.817   |	513.0  |
+|Plans	        |0.560	    |0.707  |0.625   |	198.0  |
+|Policy	|0.727      |0.786  |0.756   |	122.0  |
+|Target	    |0.741     |0.886  |0.808  |	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