Update README.md

README.md

@@ -6,6 +6,20 @@ tags:
 model-index:
 - name: CONDITIONAL-multilabel-bge
   results: []
+datasets:
+- GIZ/policy_classification
+library_name: transformers
+pipeline_tag: text-classification
+
+co2_eq_emissions:
+  emissions: 28.4522411264774
+  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: 0.702
+  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 +27,7 @@ should probably proofread and complete it, then remove this comment. -->
 
 # CONDITIONAL-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.
 It achieves the following results on the evaluation set:
 - Loss: 0.5295
 - Precision-micro: 0.5138
@@ -28,15 +42,31 @@ 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 - 
+ConditionalLabel, UnconditionalLabel - that are relevant to a particular task or application
+- **Conditional**: In context of climate policy documents if certain Target/Action/Plan/Policy commitment is being made conditionally.
+- **Unconditional**: In context of climate policy documents if certain Target/Action/Plan/Policy commitment is being made unconditionally.
 
 ## Intended uses & limitations
 
-More information needed
+The dataset sometimes does not include the sub-heading/heading which indicates that the paragraph belongs to Conditional/Unconditional category. 
+But has been copied from the relevant document from those sub-headings. This makes the assessment of Conditonality very difficult. Annotator when given only the paragraph without 
+the full long context had a difficulty in assessing the conditionality of commitments being made in paragraph.
 
 ## Training and evaluation data
 
-More information needed
+- Training Dataset: 5901
+| Class | Positive Count of Class|
+|:-------------|:--------|
+| ConditionalLabel | 1986 |
+| UnconditionalLabel | 1312 |
+
+
+- Validation Dataset: 1190
+| Class | Positive Count of Class|
+|:-------------|:--------|
+| ConditionalLabel | 192 |
+| UnconditionalLabel | 136 |
 
 ## Training procedure
 
@@ -63,10 +93,28 @@ The following hyperparameters were used during training:
 | 0.0298        | 5.0   | 1845 | 0.4971          | 0.5161          | 0.1840            | 0.5184             | 0.7317       | 0.1857         | 0.7317          | 0.6053   | 0.1829     | 0.6058      |
 | 0.0152        | 6.0   | 2214 | 0.5295          | 0.5138          | 0.1866            | 0.5169             | 0.7378       | 0.1874         | 0.7378          | 0.6058   | 0.1852     | 0.6065      |
 
+|label          | precision |recall |f1-score| support|
+|:-------------:|:---------:|:-----:|:------:|:------:|
+|ConditionalLabel	|0.490   	|0.760  |0.595  |	192.0  |
+|UnconditionalLabel	        |0.555    |0.706  |	0.621  |	136.0 |
+|
+
+### Environmental Impact
+Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
+- **Carbon Emitted**: 0.02845 kg of CO2
+- **Hours Used**: 0.702 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