
# Model Trained Using AutoNLP

- Problem type: Multi-class Classification
- Model ID: 1311135

## Validation Metrics

- Loss: 0.35616958141326904
- Accuracy: 0.8979447200566973
- Macro F1: 0.8545383956197669
- Micro F1: 0.8979447200566975
- Weighted F1: 0.8983951947775538
- Macro Precision: 0.8615833774439791
- Micro Precision: 0.8979447200566973
- Weighted Precision: 0.9013559365881655
- Macro Recall: 0.8516503001777104
- Micro Recall: 0.8979447200566973
- Weighted Recall: 0.8979447200566973


## 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/albertvillanova/autonlp-indic_glue-multi_class_classification-1e67664-1311135
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("albertvillanova/autonlp-indic_glue-multi_class_classification-1e67664-1311135", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("albertvillanova/autonlp-indic_glue-multi_class_classification-1e67664-1311135", use_auth_token=True)

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

outputs = model(**inputs)
```
{"language": "bn", "tags": "autonlp", "datasets": ["albertvillanova/autonlp-data-indic_glue-multi_class_classification-1e67664"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}]}
transformers pytorch albert text-classification autonlp bn dataset:albertvillanova/autonlp-data-indic_glue-multi_class_classification-1e67664 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #albert #text-classification #autonlp #bn #dataset-albertvillanova/autonlp-data-indic_glue-multi_class_classification-1e67664 #autotrain_compatible #endpoints_compatible #region-us 


# Model Trained Using AutoNLP

- Problem type: Multi-class Classification
- Model ID: 1311135

## Validation Metrics

- Loss: 0.35616958141326904
- Accuracy: 0.8979447200566973
- Macro F1: 0.8545383956197669
- Micro F1: 0.8979447200566975
- Weighted F1: 0.8983951947775538
- Macro Precision: 0.8615833774439791
- Micro Precision: 0.8979447200566973
- Weighted Precision: 0.9013559365881655
- Macro Recall: 0.8516503001777104
- Micro Recall: 0.8979447200566973
- Weighted Recall: 0.8979447200566973


## Usage

You can use cURL to access this model:



Or Python API:


# Model Trained Using AutoNLP

- Problem type: Multi-class Classification
- Model ID: 1311135 ## Validation Metrics

- Loss: 0.35616958141326904
- Accuracy: 0.8979447200566973
- Macro F1: 0.8545383956197669
- Micro F1: 0.8979447200566975
- Weighted F1: 0.8983951947775538
- Macro Precision: 0.8615833774439791
- Micro Precision: 0.8979447200566973
- Weighted Precision: 0.9013559365881655
- Macro Recall: 0.8516503001777104
- Micro Recall: 0.8979447200566973
- Weighted Recall: 0.8979447200566973 ## Usage

You can use cURL to access this model:



Or Python API:
TAGS
#transformers #pytorch #albert #text-classification #autonlp #bn #dataset-albertvillanova/autonlp-data-indic_glue-multi_class_classification-1e67664 #autotrain_compatible #endpoints_compatible #region-us 
 # Model Trained Using AutoNLP

- Problem type: Multi-class Classification
- Model ID: 1311135 ## Validation Metrics

- Loss: 0.35616958141326904
- Accuracy: 0.8979447200566973
- Macro F1: 0.8545383956197669
- Micro F1: 0.8979447200566975
- Weighted F1: 0.8983951947775538
- Macro Precision: 0.8615833774439791
- Micro Precision: 0.8979447200566973
- Weighted Precision: 0.9013559365881655
- Macro Recall: 0.8516503001777104
- Micro Recall: 0.8979447200566973
- Weighted Recall: 0.8979447200566973 ## Usage

You can use cURL to access this model:



Or Python API:
passage: TAGS
#transformers #pytorch #albert #text-classification #autonlp #bn #dataset-albertvillanova/autonlp-data-indic_glue-multi_class_classification-1e67664 #autotrain_compatible #endpoints_compatible #region-us 
# Model Trained Using AutoNLP

- Problem type: Multi-class Classification
- Model ID: 1311135## Validation Metrics

- Loss: 0.35616958141326904
- Accuracy: 0.8979447200566973
- Macro F1: 0.8545383956197669
- Micro F1: 0.8979447200566975
- Weighted F1: 0.8983951947775538
- Macro Precision: 0.8615833774439791
- Micro Precision: 0.8979447200566973
- Weighted Precision: 0.9013559365881655
- Macro Recall: 0.8516503001777104
- Micro Recall: 0.8979447200566973
- Weighted Recall: 0.8979447200566973## Usage

You can use cURL to access this model:



Or Python API: