Update README.md

README.md

@@ -6,6 +6,18 @@ tags:
 model-index:
 - name: SECTOR-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 +25,9 @@ should probably proofread and complete it, then remove this comment. -->
 
 # SECTOR-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.
+
+*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: 0.6028
 - Precision-micro: 0.6395
@@ -28,7 +42,9 @@ 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 Sector labels - Agriculture,Buildings,
+Coastal Zone,Cross-Cutting Area,Disaster Risk Management (DRM),Economy-wide,Education,Energy,Environment,Health,Industries,LULUCF/Forestry,Social Development,Tourism,
+Transport,Urban,Waste,Water
 
 ## Intended uses & limitations
 
@@ -36,7 +52,21 @@ 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 +94,28 @@ The following hyperparameters were used during training:
 | 0.1359        | 6.0   | 3798 | 0.5913          | 0.6349          | 0.7506            | 0.6449             | 0.7844       | 0.8676         | 0.7844          | 0.7018   | 0.7667     | 0.7057      |
 | 0.1133        | 7.0   | 4431 | 0.6028          | 0.6395          | 0.7543            | 0.6475             | 0.7762       | 0.8583         | 0.7762          | 0.7012   | 0.7655     | 0.7041      |
 
+|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  |
+
+### Environmental Impact
+Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
+- **Carbon Emitted**: 0.02335 kg of CO2
+- **Hours Used**: 0.529 hours
+
+### Training Hardware
+- **On Cloud**: yes
+- **GPU Model**: 1 x Tesla T4
+- **CPU Model**: Intel(R) Xeon(R) CPU @ 2.00GHz
+- **RAM Size**: 12.67 GB
+
 
 ### Framework versions
 
 - Transformers 4.38.1
 - Pytorch 2.1.0+cu121
 - Datasets 2.18.0
-- Tokenizers 0.15.2
+- Tokenizers 0.15.2