Update README.md

README.md

@@ -6,6 +6,19 @@ tags:
 model-index:
 - name: ADAPMIT-multilabel-bge
   results: []
+datasets:
+- GIZ/policy_classification
+library_name: sentence-transformers
+
+co2_eq_emissions:
+  emissions: 40.5174303026829
+  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: 0.994
+  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 +26,7 @@ should probably proofread and complete it, then remove this comment. -->
 
 # ADAPMIT-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 on the [Policy-Classification](https://huggingface.co/datasets/GIZ/policy_classification) dataset.
 It achieves the following results on the evaluation set:
 - Loss: 0.3101
 - Precision-micro: 0.9058
@@ -28,7 +41,8 @@ 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 +50,18 @@ More information needed
 
 ## Training and evaluation data
 
-More information needed
+- Training Dataset: 12538
+| Class | Positive Count of Class|
+|:-------------|:--------|
+| AdaptationLabel | 5439 |
+| MitigationLabel | 6659 |
+
+
+- Validation Dataset: 1190
+| Class | Positive Count of Class|
+|:-------------|:--------|
+| AdaptationLabel | 533 |
+| MitigationLabel | 604 |
 
 ## Training procedure
 
@@ -60,11 +85,26 @@ The following hyperparameters were used during training:
 | 0.1807        | 2.0   | 1568 | 0.2549          | 0.9092          | 0.8643            | 0.9094             | 0.9156       | 0.8571         | 0.9156          | 0.9124   | 0.8571     | 0.9123      |
 | 0.0955        | 3.0   | 2352 | 0.2988          | 0.9069          | 0.8660            | 0.9072             | 0.9252       | 0.8655         | 0.9252          | 0.9160   | 0.8613     | 0.9160      |
 | 0.0495        | 4.0   | 3136 | 0.3101          | 0.9058          | 0.8647            | 0.9058             | 0.9305       | 0.8693         | 0.9305          | 0.9180   | 0.8622     | 0.9180      |
-
-
+|label          | precision |recall |f1-score| support|
+|:-------------:|:---------:|:-----:|:------:|:------:|
+|AdaptationLabel	|0.910  	|0.928  |0.919  |	533.0  |
+|MitigationLabel	|0.902	    |0.932  |0.917 |	604.0  |
+
+### Environmental Impact
+Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
+- **Carbon Emitted**: 0.04051 kg of CO2
+- **Hours Used**: 0.994 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