
# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 1155542601
- CO2 Emissions (in grams): 206.60369255723003

## Validation Metrics

- Loss: 0.22105568647384644
- Accuracy: 0.9578838092484789
- Macro F1: 0.9360695960738429
- Micro F1: 0.9578838092484788
- Weighted F1: 0.957863360811612
- Macro Precision: 0.9415730549729362
- Micro Precision: 0.9578838092484789
- Weighted Precision: 0.9586754512711492
- Macro Recall: 0.9329742157218464
- Micro Recall: 0.9578838092484789
- Weighted Recall: 0.9578838092484789


## 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/ar2rpapian/autotrain-Flexport_Classification_Desc-1155542601
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("ar2rpapian/autotrain-Flexport_Classification_Desc-1155542601", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("ar2rpapian/autotrain-Flexport_Classification_Desc-1155542601", use_auth_token=True)

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

outputs = model(**inputs)
```
{"language": "en", "tags": "autotrain", "datasets": ["ar2rpapian/autotrain-data-Flexport_Classification_Desc"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 206.60369255723003}
transformers pytorch bert text-classification autotrain en dataset:ar2rpapian/autotrain-data-Flexport_Classification_Desc co2_eq_emissions autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #text-classification #autotrain #en #dataset-ar2rpapian/autotrain-data-Flexport_Classification_Desc #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 


# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 1155542601
- CO2 Emissions (in grams): 206.60369255723003

## Validation Metrics

- Loss: 0.22105568647384644
- Accuracy: 0.9578838092484789
- Macro F1: 0.9360695960738429
- Micro F1: 0.9578838092484788
- Weighted F1: 0.957863360811612
- Macro Precision: 0.9415730549729362
- Micro Precision: 0.9578838092484789
- Weighted Precision: 0.9586754512711492
- Macro Recall: 0.9329742157218464
- Micro Recall: 0.9578838092484789
- Weighted Recall: 0.9578838092484789


## Usage

You can use cURL to access this model:



Or Python API:


# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 1155542601
- CO2 Emissions (in grams): 206.60369255723003 ## Validation Metrics

- Loss: 0.22105568647384644
- Accuracy: 0.9578838092484789
- Macro F1: 0.9360695960738429
- Micro F1: 0.9578838092484788
- Weighted F1: 0.957863360811612
- Macro Precision: 0.9415730549729362
- Micro Precision: 0.9578838092484789
- Weighted Precision: 0.9586754512711492
- Macro Recall: 0.9329742157218464
- Micro Recall: 0.9578838092484789
- Weighted Recall: 0.9578838092484789 ## Usage

You can use cURL to access this model:



Or Python API:
TAGS
#transformers #pytorch #bert #text-classification #autotrain #en #dataset-ar2rpapian/autotrain-data-Flexport_Classification_Desc #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
 # Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 1155542601
- CO2 Emissions (in grams): 206.60369255723003 ## Validation Metrics

- Loss: 0.22105568647384644
- Accuracy: 0.9578838092484789
- Macro F1: 0.9360695960738429
- Micro F1: 0.9578838092484788
- Weighted F1: 0.957863360811612
- Macro Precision: 0.9415730549729362
- Micro Precision: 0.9578838092484789
- Weighted Precision: 0.9586754512711492
- Macro Recall: 0.9329742157218464
- Micro Recall: 0.9578838092484789
- Weighted Recall: 0.9578838092484789 ## Usage

You can use cURL to access this model:



Or Python API:
passage: TAGS
#transformers #pytorch #bert #text-classification #autotrain #en #dataset-ar2rpapian/autotrain-data-Flexport_Classification_Desc #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 1155542601
- CO2 Emissions (in grams): 206.60369255723003## Validation Metrics

- Loss: 0.22105568647384644
- Accuracy: 0.9578838092484789
- Macro F1: 0.9360695960738429
- Micro F1: 0.9578838092484788
- Weighted F1: 0.957863360811612
- Macro Precision: 0.9415730549729362
- Micro Precision: 0.9578838092484789
- Weighted Precision: 0.9586754512711492
- Macro Recall: 0.9329742157218464
- Micro Recall: 0.9578838092484789
- Weighted Recall: 0.9578838092484789## Usage

You can use cURL to access this model:



Or Python API: