
# Model Trained Using AutoNLP

- Problem type: Multi-class Classification
- Model ID: 452311620
- CO2 Emissions (in grams): 208.0823957145878

## Validation Metrics

- Loss: 0.5259971022605896
- Accuracy: 0.8767479025169796
- Macro F1: 0.8618813750734912
- Micro F1: 0.8767479025169796
- Weighted F1: 0.8742964006840133
- Macro Precision: 0.8627700506991158
- Micro Precision: 0.8767479025169796
- Weighted Precision: 0.8755603985289852
- Macro Recall: 0.8662183006750934
- Micro Recall: 0.8767479025169796
- Weighted Recall: 0.8767479025169796


## 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/Smone55/autonlp-au_topics-452311620
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("Smone55/autonlp-au_topics-452311620", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("Smone55/autonlp-au_topics-452311620", use_auth_token=True)

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

outputs = model(**inputs)
```
{"language": "en", "tags": "autonlp", "datasets": ["Smone55/autonlp-data-au_topics"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 208.0823957145878}
transformers pytorch bert text-classification autonlp en dataset:Smone55/autonlp-data-au_topics co2_eq_emissions autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #text-classification #autonlp #en #dataset-Smone55/autonlp-data-au_topics #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 


# Model Trained Using AutoNLP

- Problem type: Multi-class Classification
- Model ID: 452311620
- CO2 Emissions (in grams): 208.0823957145878

## Validation Metrics

- Loss: 0.5259971022605896
- Accuracy: 0.8767479025169796
- Macro F1: 0.8618813750734912
- Micro F1: 0.8767479025169796
- Weighted F1: 0.8742964006840133
- Macro Precision: 0.8627700506991158
- Micro Precision: 0.8767479025169796
- Weighted Precision: 0.8755603985289852
- Macro Recall: 0.8662183006750934
- Micro Recall: 0.8767479025169796
- Weighted Recall: 0.8767479025169796


## Usage

You can use cURL to access this model:



Or Python API:


# Model Trained Using AutoNLP

- Problem type: Multi-class Classification
- Model ID: 452311620
- CO2 Emissions (in grams): 208.0823957145878 ## Validation Metrics

- Loss: 0.5259971022605896
- Accuracy: 0.8767479025169796
- Macro F1: 0.8618813750734912
- Micro F1: 0.8767479025169796
- Weighted F1: 0.8742964006840133
- Macro Precision: 0.8627700506991158
- Micro Precision: 0.8767479025169796
- Weighted Precision: 0.8755603985289852
- Macro Recall: 0.8662183006750934
- Micro Recall: 0.8767479025169796
- Weighted Recall: 0.8767479025169796 ## Usage

You can use cURL to access this model:



Or Python API:
TAGS
#transformers #pytorch #bert #text-classification #autonlp #en #dataset-Smone55/autonlp-data-au_topics #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
 # Model Trained Using AutoNLP

- Problem type: Multi-class Classification
- Model ID: 452311620
- CO2 Emissions (in grams): 208.0823957145878 ## Validation Metrics

- Loss: 0.5259971022605896
- Accuracy: 0.8767479025169796
- Macro F1: 0.8618813750734912
- Micro F1: 0.8767479025169796
- Weighted F1: 0.8742964006840133
- Macro Precision: 0.8627700506991158
- Micro Precision: 0.8767479025169796
- Weighted Precision: 0.8755603985289852
- Macro Recall: 0.8662183006750934
- Micro Recall: 0.8767479025169796
- Weighted Recall: 0.8767479025169796 ## Usage

You can use cURL to access this model:



Or Python API:
passage: TAGS
#transformers #pytorch #bert #text-classification #autonlp #en #dataset-Smone55/autonlp-data-au_topics #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
# Model Trained Using AutoNLP

- Problem type: Multi-class Classification
- Model ID: 452311620
- CO2 Emissions (in grams): 208.0823957145878## Validation Metrics

- Loss: 0.5259971022605896
- Accuracy: 0.8767479025169796
- Macro F1: 0.8618813750734912
- Micro F1: 0.8767479025169796
- Weighted F1: 0.8742964006840133
- Macro Precision: 0.8627700506991158
- Micro Precision: 0.8767479025169796
- Weighted Precision: 0.8755603985289852
- Macro Recall: 0.8662183006750934
- Micro Recall: 0.8767479025169796
- Weighted Recall: 0.8767479025169796## Usage

You can use cURL to access this model:



Or Python API: