
# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 1104340243
- CO2 Emissions (in grams): 27.982443349742287

## Validation Metrics

- Loss: 0.9584922790527344
- Accuracy: 0.5843
- Macro F1: 0.5801009597024507
- Micro F1: 0.5843
- Weighted F1: 0.5792137097243996
- Macro Precision: 0.5897236028586046
- Micro Precision: 0.5843
- Weighted Precision: 0.5896188517045103
- Macro Recall: 0.5857983081566331
- Micro Recall: 0.5843
- Weighted Recall: 0.5843


## 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/mbyanfei/autotrain-amazon-shoe-reviews-classification-1104340243
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("mbyanfei/autotrain-amazon-shoe-reviews-classification-1104340243", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("mbyanfei/autotrain-amazon-shoe-reviews-classification-1104340243", use_auth_token=True)

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

outputs = model(**inputs)
```
{"language": "en", "tags": "autotrain", "datasets": ["mbyanfei/autotrain-data-amazon-shoe-reviews-classification"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 27.982443349742287}
transformers pytorch roberta text-classification autotrain en dataset:mbyanfei/autotrain-data-amazon-shoe-reviews-classification co2_eq_emissions autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #roberta #text-classification #autotrain #en #dataset-mbyanfei/autotrain-data-amazon-shoe-reviews-classification #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 


# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 1104340243
- CO2 Emissions (in grams): 27.982443349742287

## Validation Metrics

- Loss: 0.9584922790527344
- Accuracy: 0.5843
- Macro F1: 0.5801009597024507
- Micro F1: 0.5843
- Weighted F1: 0.5792137097243996
- Macro Precision: 0.5897236028586046
- Micro Precision: 0.5843
- Weighted Precision: 0.5896188517045103
- Macro Recall: 0.5857983081566331
- Micro Recall: 0.5843
- Weighted Recall: 0.5843


## Usage

You can use cURL to access this model:



Or Python API:


# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 1104340243
- CO2 Emissions (in grams): 27.982443349742287 ## Validation Metrics

- Loss: 0.9584922790527344
- Accuracy: 0.5843
- Macro F1: 0.5801009597024507
- Micro F1: 0.5843
- Weighted F1: 0.5792137097243996
- Macro Precision: 0.5897236028586046
- Micro Precision: 0.5843
- Weighted Precision: 0.5896188517045103
- Macro Recall: 0.5857983081566331
- Micro Recall: 0.5843
- Weighted Recall: 0.5843 ## Usage

You can use cURL to access this model:



Or Python API:
TAGS
#transformers #pytorch #roberta #text-classification #autotrain #en #dataset-mbyanfei/autotrain-data-amazon-shoe-reviews-classification #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
 # Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 1104340243
- CO2 Emissions (in grams): 27.982443349742287 ## Validation Metrics

- Loss: 0.9584922790527344
- Accuracy: 0.5843
- Macro F1: 0.5801009597024507
- Micro F1: 0.5843
- Weighted F1: 0.5792137097243996
- Macro Precision: 0.5897236028586046
- Micro Precision: 0.5843
- Weighted Precision: 0.5896188517045103
- Macro Recall: 0.5857983081566331
- Micro Recall: 0.5843
- Weighted Recall: 0.5843 ## Usage

You can use cURL to access this model:



Or Python API:
passage: TAGS
#transformers #pytorch #roberta #text-classification #autotrain #en #dataset-mbyanfei/autotrain-data-amazon-shoe-reviews-classification #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 1104340243
- CO2 Emissions (in grams): 27.982443349742287## Validation Metrics

- Loss: 0.9584922790527344
- Accuracy: 0.5843
- Macro F1: 0.5801009597024507
- Micro F1: 0.5843
- Weighted F1: 0.5792137097243996
- Macro Precision: 0.5897236028586046
- Micro Precision: 0.5843
- Weighted Precision: 0.5896188517045103
- Macro Recall: 0.5857983081566331
- Micro Recall: 0.5843
- Weighted Recall: 0.5843## Usage

You can use cURL to access this model:



Or Python API: