
# Model Trained Using AutoNLP

- Problem type: Multi-class Classification
- Model ID: 536415182
- CO2 Emissions (in grams): 1.268309634217171

## Validation Metrics

- Loss: 0.44733062386512756
- Accuracy: 0.8873239436619719
- Macro F1: 0.8859416445623343
- Micro F1: 0.8873239436619719
- Weighted F1: 0.8864646766540891
- Macro Precision: 0.8848522167487685
- Micro Precision: 0.8873239436619719
- Weighted Precision: 0.8883299798792756
- Macro Recall: 0.8908045977011494
- Micro Recall: 0.8873239436619719
- Weighted Recall: 0.8873239436619719


## 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/alperiox/autonlp-user-review-classification-536415182
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("alperiox/autonlp-user-review-classification-536415182", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("alperiox/autonlp-user-review-classification-536415182", use_auth_token=True)

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

outputs = model(**inputs)
```
{"language": "en", "tags": "autonlp", "datasets": ["alperiox/autonlp-data-user-review-classification"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 1.268309634217171}
transformers pytorch bert text-classification autonlp en dataset:alperiox/autonlp-data-user-review-classification co2_eq_emissions autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #text-classification #autonlp #en #dataset-alperiox/autonlp-data-user-review-classification #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 


# Model Trained Using AutoNLP

- Problem type: Multi-class Classification
- Model ID: 536415182
- CO2 Emissions (in grams): 1.268309634217171

## Validation Metrics

- Loss: 0.44733062386512756
- Accuracy: 0.8873239436619719
- Macro F1: 0.8859416445623343
- Micro F1: 0.8873239436619719
- Weighted F1: 0.8864646766540891
- Macro Precision: 0.8848522167487685
- Micro Precision: 0.8873239436619719
- Weighted Precision: 0.8883299798792756
- Macro Recall: 0.8908045977011494
- Micro Recall: 0.8873239436619719
- Weighted Recall: 0.8873239436619719


## Usage

You can use cURL to access this model:



Or Python API:


# Model Trained Using AutoNLP

- Problem type: Multi-class Classification
- Model ID: 536415182
- CO2 Emissions (in grams): 1.268309634217171 ## Validation Metrics

- Loss: 0.44733062386512756
- Accuracy: 0.8873239436619719
- Macro F1: 0.8859416445623343
- Micro F1: 0.8873239436619719
- Weighted F1: 0.8864646766540891
- Macro Precision: 0.8848522167487685
- Micro Precision: 0.8873239436619719
- Weighted Precision: 0.8883299798792756
- Macro Recall: 0.8908045977011494
- Micro Recall: 0.8873239436619719
- Weighted Recall: 0.8873239436619719 ## Usage

You can use cURL to access this model:



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

- Problem type: Multi-class Classification
- Model ID: 536415182
- CO2 Emissions (in grams): 1.268309634217171 ## Validation Metrics

- Loss: 0.44733062386512756
- Accuracy: 0.8873239436619719
- Macro F1: 0.8859416445623343
- Micro F1: 0.8873239436619719
- Weighted F1: 0.8864646766540891
- Macro Precision: 0.8848522167487685
- Micro Precision: 0.8873239436619719
- Weighted Precision: 0.8883299798792756
- Macro Recall: 0.8908045977011494
- Micro Recall: 0.8873239436619719
- Weighted Recall: 0.8873239436619719 ## Usage

You can use cURL to access this model:



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

- Problem type: Multi-class Classification
- Model ID: 536415182
- CO2 Emissions (in grams): 1.268309634217171## Validation Metrics

- Loss: 0.44733062386512756
- Accuracy: 0.8873239436619719
- Macro F1: 0.8859416445623343
- Micro F1: 0.8873239436619719
- Weighted F1: 0.8864646766540891
- Macro Precision: 0.8848522167487685
- Micro Precision: 0.8873239436619719
- Weighted Precision: 0.8883299798792756
- Macro Recall: 0.8908045977011494
- Micro Recall: 0.8873239436619719
- Weighted Recall: 0.8873239436619719## Usage

You can use cURL to access this model:



Or Python API: