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# mpnet-multilabel-sector-classifier

This model is a fine-tuned version of [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2273
- Precision Micro: 0.8075
- Precision Weighted: 0.8110
- Precision Samples: 0.8365
- Recall Micro: 0.8897
- Recall Weighted: 0.8897
- Recall Samples: 0.8922
- F1-score: 0.8464

## Model description

This model is trained for performing **Multi Label Sector Classification**. 


### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 6.9e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 200
- num_epochs: 8
- weight_decay: 0.001
- gradient_acumulation_steps: 1

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision Micro | Precision Weighted | Precision Samples | Recall Micro | Recall Weighted | Recall Samples | F1-score |
|:-------------:|:-----:|:----:|:---------------:|:---------------:|:------------------:|:-----------------:|:------------:|:---------------:|:--------------:|:--------:|
| 0.4478        | 1.0   | 897  | 0.2277          | 0.6731          | 0.7183             | 0.7460            | 0.8822       | 0.8822          | 0.8989         | 0.7871   |
| 0.2241        | 2.0   | 1794 | 0.1862          | 0.7088          | 0.7485             | 0.7754            | 0.8933       | 0.8933          | 0.9110         | 0.8108   |
| 0.1647        | 3.0   | 2691 | 0.2025          | 0.6785          | 0.7023             | 0.7634            | 0.9124       | 0.9124          | 0.9252         | 0.8077   |
| 0.1232        | 4.0   | 3588 | 0.1839          | 0.7274          | 0.7322             | 0.7976            | 0.9029       | 0.9029          | 0.9134         | 0.8286   |
| 0.0899        | 5.0   | 4485 | 0.1889          | 0.7919          | 0.8007             | 0.8350            | 0.8909       | 0.8909          | 0.9060         | 0.8483   |
| 0.0653        | 6.0   | 5382 | 0.2039          | 0.7478          | 0.7544             | 0.8098            | 0.8973       | 0.8973          | 0.9114         | 0.8346   |
| 0.0462        | 7.0   | 6279 | 0.2149          | 0.7447          | 0.7500             | 0.8060            | 0.8989       | 0.8989          | 0.9107         | 0.8323   |
| 0.0336        | 8.0   | 7176 | 0.2181          | 0.7733          | 0.7780             | 0.8221            | 0.8909       | 0.8909          | 0.9031         | 0.8400   |

## Environmental Impact

*Carbon emissions were estimated using the [codecarbon](https://github.com/mlco2/codecarbon). The carbon emission reported are incluidng the hyperparamter search performed on subset of training data*. 

- **Hardware Type:**  16GB T4
- **Hours used:** 3
- **Cloud Provider:** Google Colab
- **Carbon Emitted** : 0.276132
### Framework versions

- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
{"license": "apache-2.0", "library_name": "transformers", "tags": ["generated_from_trainer"], "datasets": ["GIZ/sector_data"], "co2_eq_emissions": 0.276132, "widget": [{"text": "Forestry, forestry and wildlife: Vulnerability will be globally high to very high in zones 4 and 5, high to medium in the rest of the country but with strong trends in woodlands (droughts, extreme events);. - Water, sanitation and health: Vulnerability will be globally strong to very strong in zones 4 and 5, strong to medium in the rest of the country but with strong trends in the forested massifs (drought, floods and ground movement)", "example_title": "Disaster Risk Management (DRM), Water, Environment"}, {"text": "Change fiscal policies on fossil fuel by 2025 to enable the transition to 100% renewable energy generation in the transportation sector", "example_title": "Transport, Energy"}, {"text": "Implementation of the electro-optical channel regulations for the distributed electricians, technicians in other regions and cities. 2- An integrated nationalization that complements the use of smart meter technology inside buildings. 3- Integrated solar photovoltaic in buildings. 4- Support your company and use it from local women s clubs and local producers. Waste. 1- Setting up waste management laws, which encourages the transfer of waste into bottles and bottles, we will burn the waste streams and reduce waste. 1- We use the appropriate regulation in our time to remove electrical and electrical rations from waste. 2- An integrated application for waste management. 3- Investing fire methane on landfill sites. Farming. 1- Nannnai to protect and increase the natural gaunanat", "example_title": "Social Development, Waste, Urban, Buildings"}, {"text": "Distribution of GHG Emissions by Gas in 2018Determined Contribution at the National Level Directorate of the Environment Evolution of GHG Emissions by Gas between 1990 and 2018Determined Contribution at the National Level Directorate of the Environment 2.2 Objectives for the Reduction of Greenhouse Gas Emissions by 2030 The Principality of Monaco has set itself the objective, within the framework of this National Contribution, of reducing its greenhouse gas emissions by 55% by 2030.Determined Contribution at the National Level Directorate of the Environment 2.3 Main Policies and Measures In order to achieve its objectives by 2030, the Principality of Monaco has already implemented important policies and measures", "example_title": "Economy-wide"}], "model-index": [{"name": "mpnet-multilabel-sector-classifier", "results": []}]}
transformers pytorch mpnet text-classification generated_from_trainer dataset:GIZ/sector_data license:apache-2.0 co2_eq_emissions autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #mpnet #text-classification #generated_from_trainer #dataset-GIZ/sector_data #license-apache-2.0 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 

mpnet-multilabel-sector-classifier
==================================


This model is a fine-tuned version of sentence-transformers/all-mpnet-base-v2 on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 0.2273
* Precision Micro: 0.8075
* Precision Weighted: 0.8110
* Precision Samples: 0.8365
* Recall Micro: 0.8897
* Recall Weighted: 0.8897
* Recall Samples: 0.8922
* F1-score: 0.8464


Model description
-----------------


This model is trained for performing Multi Label Sector Classification.


### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 6.9e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 200
* num\_epochs: 8
* weight\_decay: 0.001
* gradient\_acumulation\_steps: 1


### Training results



Environmental Impact
--------------------


*Carbon emissions were estimated using the codecarbon. The carbon emission reported are incluidng the hyperparamter search performed on subset of training data*.


* Hardware Type: 16GB T4
* Hours used: 3
* Cloud Provider: Google Colab
* Carbon Emitted : 0.276132


### Framework versions


* Transformers 4.28.0
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 6.9e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 200
* num\_epochs: 8
* weight\_decay: 0.001
* gradient\_acumulation\_steps: 1 ### Training results



Environmental Impact
--------------------


*Carbon emissions were estimated using the codecarbon. The carbon emission reported are incluidng the hyperparamter search performed on subset of training data*.


* Hardware Type: 16GB T4
* Hours used: 3
* Cloud Provider: Google Colab
* Carbon Emitted : 0.276132 ### Framework versions


* Transformers 4.28.0
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #mpnet #text-classification #generated_from_trainer #dataset-GIZ/sector_data #license-apache-2.0 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 6.9e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 200
* num\_epochs: 8
* weight\_decay: 0.001
* gradient\_acumulation\_steps: 1 ### Training results



Environmental Impact
--------------------


*Carbon emissions were estimated using the codecarbon. The carbon emission reported are incluidng the hyperparamter search performed on subset of training data*.


* Hardware Type: 16GB T4
* Hours used: 3
* Cloud Provider: Google Colab
* Carbon Emitted : 0.276132 ### Framework versions


* Transformers 4.28.0
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3