# Class
0 - None <br>
1 - Metrics and Targets  <br>
2 - Strategy  <br>
3 - Risk Management  <br>
4 - Governance  <br>
# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 55122128742
- CO2 Emissions (in grams): 0.0015

## Validation Metrics

- Loss: 0.646
- Accuracy: 0.777
- Macro F1: 0.727
- Micro F1: 0.777
- Weighted F1: 0.779
- Macro Precision: 0.734
- Micro Precision: 0.777
- Weighted Precision: 0.786
- Macro Recall: 0.731
- Micro Recall: 0.777
- Weighted Recall: 0.777


## Usage

You can use cURL to access this model:

```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/Sachinkelenjaguri/climate-tcfd-recommendation
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("Sachinkelenjaguri/climate-tcfd-recommendation", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("Sachinkelenjaguri/climate-tcfd-recommendation", use_auth_token=True)

inputs = tokenizer("I love AutoTrain", return_tensors="pt")

outputs = model(**inputs)
```
{"language": ["unk"], "tags": ["autotrain", "text-classification"], "datasets": ["Sachinkelenjaguri/autotrain-data-climate-tcfd-recommendation"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": {"emissions": 0.0015416078395342335}}
transformers pytorch distilbert text-classification autotrain unk dataset:Sachinkelenjaguri/autotrain-data-climate-tcfd-recommendation co2_eq_emissions autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #distilbert #text-classification #autotrain #unk #dataset-Sachinkelenjaguri/autotrain-data-climate-tcfd-recommendation #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 

# Class
0 - None <br>
1 - Metrics and Targets  <br>
2 - Strategy  <br>
3 - Risk Management  <br>
4 - Governance  <br>
# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 55122128742
- CO2 Emissions (in grams): 0.0015

## Validation Metrics

- Loss: 0.646
- Accuracy: 0.777
- Macro F1: 0.727
- Micro F1: 0.777
- Weighted F1: 0.779
- Macro Precision: 0.734
- Micro Precision: 0.777
- Weighted Precision: 0.786
- Macro Recall: 0.731
- Micro Recall: 0.777
- Weighted Recall: 0.777


## Usage

You can use cURL to access this model:



Or Python API:


# Class
0 - None <br>
1 - Metrics and Targets  <br>
2 - Strategy  <br>
3 - Risk Management  <br>
4 - Governance  <br> # Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 55122128742
- CO2 Emissions (in grams): 0.0015 ## Validation Metrics

- Loss: 0.646
- Accuracy: 0.777
- Macro F1: 0.727
- Micro F1: 0.777
- Weighted F1: 0.779
- Macro Precision: 0.734
- Micro Precision: 0.777
- Weighted Precision: 0.786
- Macro Recall: 0.731
- Micro Recall: 0.777
- Weighted Recall: 0.777 ## Usage

You can use cURL to access this model:



Or Python API:
TAGS
#transformers #pytorch #distilbert #text-classification #autotrain #unk #dataset-Sachinkelenjaguri/autotrain-data-climate-tcfd-recommendation #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
 # Class
0 - None <br>
1 - Metrics and Targets  <br>
2 - Strategy  <br>
3 - Risk Management  <br>
4 - Governance  <br> # Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 55122128742
- CO2 Emissions (in grams): 0.0015 ## Validation Metrics

- Loss: 0.646
- Accuracy: 0.777
- Macro F1: 0.727
- Micro F1: 0.777
- Weighted F1: 0.779
- Macro Precision: 0.734
- Micro Precision: 0.777
- Weighted Precision: 0.786
- Macro Recall: 0.731
- Micro Recall: 0.777
- Weighted Recall: 0.777 ## Usage

You can use cURL to access this model:



Or Python API: