
# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- fine tuned with indonlp/indonlu dataset. (10000 rows from https://huggingface.co/datasets/indonlp/indonlu/viewer/smsa/train)
- Model ID: 2885384370
- CO2 Emissions (in grams): 5.3951

## Validation Metrics

- Loss: 0.270
- Accuracy: 0.900
- Macro F1: 0.866
- Micro F1: 0.900
- Weighted F1: 0.899
- Macro Precision: 0.874
- Micro Precision: 0.900
- Weighted Precision: 0.899
- Macro Recall: 0.859
- Micro Recall: 0.900
- Weighted Recall: 0.900


## 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/mkhairil/autotrain-text-sentiment-indonlu-smse-2885384370
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("mkhairil/autotrain-text-sentiment-indonlu-smse-2885384370", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("mkhairil/autotrain-text-sentiment-indonlu-smse-2885384370", use_auth_token=True)

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

outputs = model(**inputs)
```
{"language": ["unk", "id"], "license": "apache-2.0", "tags": ["autotrain", "text-classification"], "datasets": ["mkhairil/autotrain-data-text-sentiment-indonlu-smse"], "widget": [{"text": "ini filmnya keren banget"}], "co2_eq_emissions": {"emissions": 5.395117116799661}}
transformers pytorch safetensors bert text-classification autotrain unk id dataset:mkhairil/autotrain-data-text-sentiment-indonlu-smse license:apache-2.0 co2_eq_emissions autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #safetensors #bert #text-classification #autotrain #unk #id #dataset-mkhairil/autotrain-data-text-sentiment-indonlu-smse #license-apache-2.0 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #has_space #region-us 


# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- fine tuned with indonlp/indonlu dataset. (10000 rows from URL
- Model ID: 2885384370
- CO2 Emissions (in grams): 5.3951

## Validation Metrics

- Loss: 0.270
- Accuracy: 0.900
- Macro F1: 0.866
- Micro F1: 0.900
- Weighted F1: 0.899
- Macro Precision: 0.874
- Micro Precision: 0.900
- Weighted Precision: 0.899
- Macro Recall: 0.859
- Micro Recall: 0.900
- Weighted Recall: 0.900


## Usage

You can use cURL to access this model:



Or Python API:


# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- fine tuned with indonlp/indonlu dataset. (10000 rows from URL
- Model ID: 2885384370
- CO2 Emissions (in grams): 5.3951 ## Validation Metrics

- Loss: 0.270
- Accuracy: 0.900
- Macro F1: 0.866
- Micro F1: 0.900
- Weighted F1: 0.899
- Macro Precision: 0.874
- Micro Precision: 0.900
- Weighted Precision: 0.899
- Macro Recall: 0.859
- Micro Recall: 0.900
- Weighted Recall: 0.900 ## Usage

You can use cURL to access this model:



Or Python API:
TAGS
#transformers #pytorch #safetensors #bert #text-classification #autotrain #unk #id #dataset-mkhairil/autotrain-data-text-sentiment-indonlu-smse #license-apache-2.0 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #has_space #region-us 
 # Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- fine tuned with indonlp/indonlu dataset. (10000 rows from URL
- Model ID: 2885384370
- CO2 Emissions (in grams): 5.3951 ## Validation Metrics

- Loss: 0.270
- Accuracy: 0.900
- Macro F1: 0.866
- Micro F1: 0.900
- Weighted F1: 0.899
- Macro Precision: 0.874
- Micro Precision: 0.900
- Weighted Precision: 0.899
- Macro Recall: 0.859
- Micro Recall: 0.900
- Weighted Recall: 0.900 ## Usage

You can use cURL to access this model:



Or Python API:
TAGS
#transformers #pytorch #safetensors #bert #text-classification #autotrain #unk #id #dataset-mkhairil/autotrain-data-text-sentiment-indonlu-smse #license-apache-2.0 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #has_space #region-us 
# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- fine tuned with indonlp/indonlu dataset. (10000 rows from URL
- Model ID: 2885384370
- CO2 Emissions (in grams): 5.3951## Validation Metrics

- Loss: 0.270
- Accuracy: 0.900
- Macro F1: 0.866
- Micro F1: 0.900
- Weighted F1: 0.899
- Macro Precision: 0.874
- Micro Precision: 0.900
- Weighted Precision: 0.899
- Macro Recall: 0.859
- Micro Recall: 0.900
- Weighted Recall: 0.900## Usage

You can use cURL to access this model:



Or Python API: