
# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 753423076
- CO2 Emissions (in grams): 0.05286505617263864

## Validation Metrics

- Loss: 0.539419412612915
- Accuracy: 0.7616387337057728
- Macro F1: 0.6428050387135232
- Micro F1: 0.761638733705773
- Weighted F1: 0.7592341595725172
- Macro Precision: 0.6606534010647378
- Micro Precision: 0.7616387337057728
- Weighted Precision: 0.7575825822976101
- Macro Recall: 0.6293404928847536
- Micro Recall: 0.7616387337057728
- Weighted Recall: 0.7616387337057728


## 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/crcb/autotrain-imp_hs-753423076
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("crcb/autotrain-imp_hs-753423076", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("crcb/autotrain-imp_hs-753423076", use_auth_token=True)

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

outputs = model(**inputs)
```
{"language": "en", "tags": "autotrain", "datasets": ["crcb/autotrain-data-imp_hs"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 0.05286505617263864}
transformers pytorch bert text-classification autotrain en dataset:crcb/autotrain-data-imp_hs co2_eq_emissions autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #text-classification #autotrain #en #dataset-crcb/autotrain-data-imp_hs #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 


# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 753423076
- CO2 Emissions (in grams): 0.05286505617263864

## Validation Metrics

- Loss: 0.539419412612915
- Accuracy: 0.7616387337057728
- Macro F1: 0.6428050387135232
- Micro F1: 0.761638733705773
- Weighted F1: 0.7592341595725172
- Macro Precision: 0.6606534010647378
- Micro Precision: 0.7616387337057728
- Weighted Precision: 0.7575825822976101
- Macro Recall: 0.6293404928847536
- Micro Recall: 0.7616387337057728
- Weighted Recall: 0.7616387337057728


## Usage

You can use cURL to access this model:



Or Python API:


# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 753423076
- CO2 Emissions (in grams): 0.05286505617263864 ## Validation Metrics

- Loss: 0.539419412612915
- Accuracy: 0.7616387337057728
- Macro F1: 0.6428050387135232
- Micro F1: 0.761638733705773
- Weighted F1: 0.7592341595725172
- Macro Precision: 0.6606534010647378
- Micro Precision: 0.7616387337057728
- Weighted Precision: 0.7575825822976101
- Macro Recall: 0.6293404928847536
- Micro Recall: 0.7616387337057728
- Weighted Recall: 0.7616387337057728 ## Usage

You can use cURL to access this model:



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

- Problem type: Multi-class Classification
- Model ID: 753423076
- CO2 Emissions (in grams): 0.05286505617263864 ## Validation Metrics

- Loss: 0.539419412612915
- Accuracy: 0.7616387337057728
- Macro F1: 0.6428050387135232
- Micro F1: 0.761638733705773
- Weighted F1: 0.7592341595725172
- Macro Precision: 0.6606534010647378
- Micro Precision: 0.7616387337057728
- Weighted Precision: 0.7575825822976101
- Macro Recall: 0.6293404928847536
- Micro Recall: 0.7616387337057728
- Weighted Recall: 0.7616387337057728 ## Usage

You can use cURL to access this model:



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

- Problem type: Multi-class Classification
- Model ID: 753423076
- CO2 Emissions (in grams): 0.05286505617263864## Validation Metrics

- Loss: 0.539419412612915
- Accuracy: 0.7616387337057728
- Macro F1: 0.6428050387135232
- Micro F1: 0.761638733705773
- Weighted F1: 0.7592341595725172
- Macro Precision: 0.6606534010647378
- Micro Precision: 0.7616387337057728
- Weighted Precision: 0.7575825822976101
- Macro Recall: 0.6293404928847536
- Micro Recall: 0.7616387337057728
- Weighted Recall: 0.7616387337057728## Usage

You can use cURL to access this model:



Or Python API: