
# Model Trained Using AutoNLP

- Problem type: Multi-class Classification
- Model ID: 615517563
- CO2 Emissions (in grams): 1.9057858628956459

## Validation Metrics

- Loss: 0.6990908980369568
- Accuracy: 0.695364238410596
- Macro F1: 0.6088819062581828
- Micro F1: 0.695364238410596
- Weighted F1: 0.677326207350606
- Macro Precision: 0.6945099492363175
- Micro Precision: 0.695364238410596
- Weighted Precision: 0.6938596845881614
- Macro Recall: 0.5738408020723632
- Micro Recall: 0.695364238410596
- Weighted Recall: 0.695364238410596


## 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 AutoNLP"}' https://api-inference.huggingface.co/models/abhishek/autonlp-swahili-sentiment-615517563
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("abhishek/autonlp-swahili-sentiment-615517563", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("abhishek/autonlp-swahili-sentiment-615517563", use_auth_token=True)

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

outputs = model(**inputs)
```
{"language": "unk", "tags": "autonlp", "datasets": ["abhishek/autonlp-data-swahili-sentiment"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 1.9057858628956459}
transformers pytorch bert text-classification autonlp unk dataset:abhishek/autonlp-data-swahili-sentiment co2_eq_emissions autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #text-classification #autonlp #unk #dataset-abhishek/autonlp-data-swahili-sentiment #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 


# Model Trained Using AutoNLP

- Problem type: Multi-class Classification
- Model ID: 615517563
- CO2 Emissions (in grams): 1.9057858628956459

## Validation Metrics

- Loss: 0.6990908980369568
- Accuracy: 0.695364238410596
- Macro F1: 0.6088819062581828
- Micro F1: 0.695364238410596
- Weighted F1: 0.677326207350606
- Macro Precision: 0.6945099492363175
- Micro Precision: 0.695364238410596
- Weighted Precision: 0.6938596845881614
- Macro Recall: 0.5738408020723632
- Micro Recall: 0.695364238410596
- Weighted Recall: 0.695364238410596


## Usage

You can use cURL to access this model:



Or Python API:


# Model Trained Using AutoNLP

- Problem type: Multi-class Classification
- Model ID: 615517563
- CO2 Emissions (in grams): 1.9057858628956459 ## Validation Metrics

- Loss: 0.6990908980369568
- Accuracy: 0.695364238410596
- Macro F1: 0.6088819062581828
- Micro F1: 0.695364238410596
- Weighted F1: 0.677326207350606
- Macro Precision: 0.6945099492363175
- Micro Precision: 0.695364238410596
- Weighted Precision: 0.6938596845881614
- Macro Recall: 0.5738408020723632
- Micro Recall: 0.695364238410596
- Weighted Recall: 0.695364238410596 ## Usage

You can use cURL to access this model:



Or Python API:
TAGS
#transformers #pytorch #bert #text-classification #autonlp #unk #dataset-abhishek/autonlp-data-swahili-sentiment #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
 # Model Trained Using AutoNLP

- Problem type: Multi-class Classification
- Model ID: 615517563
- CO2 Emissions (in grams): 1.9057858628956459 ## Validation Metrics

- Loss: 0.6990908980369568
- Accuracy: 0.695364238410596
- Macro F1: 0.6088819062581828
- Micro F1: 0.695364238410596
- Weighted F1: 0.677326207350606
- Macro Precision: 0.6945099492363175
- Micro Precision: 0.695364238410596
- Weighted Precision: 0.6938596845881614
- Macro Recall: 0.5738408020723632
- Micro Recall: 0.695364238410596
- Weighted Recall: 0.695364238410596 ## Usage

You can use cURL to access this model:



Or Python API:
passage: TAGS
#transformers #pytorch #bert #text-classification #autonlp #unk #dataset-abhishek/autonlp-data-swahili-sentiment #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
# Model Trained Using AutoNLP

- Problem type: Multi-class Classification
- Model ID: 615517563
- CO2 Emissions (in grams): 1.9057858628956459## Validation Metrics

- Loss: 0.6990908980369568
- Accuracy: 0.695364238410596
- Macro F1: 0.6088819062581828
- Micro F1: 0.695364238410596
- Weighted F1: 0.677326207350606
- Macro Precision: 0.6945099492363175
- Micro Precision: 0.695364238410596
- Weighted Precision: 0.6938596845881614
- Macro Recall: 0.5738408020723632
- Micro Recall: 0.695364238410596
- Weighted Recall: 0.695364238410596## Usage

You can use cURL to access this model:



Or Python API: