
# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 748922872
- CO2 Emissions (in grams): 31.11935827749309

## Validation Metrics

- Loss: 0.17039568722248077
- Accuracy: 0.93625
- Macro F1: 0.9075787460059076
- Micro F1: 0.93625
- Weighted F1: 0.9371621543264445
- Macro Precision: 0.8945117620407296
- Micro Precision: 0.93625
- Weighted Precision: 0.9433589433926076
- Macro Recall: 0.9323604226458176
- Micro Recall: 0.93625
- Weighted Recall: 0.93625


## 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-go_emo-748922872
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("crcb/autotrain-go_emo-748922872", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("crcb/autotrain-go_emo-748922872", use_auth_token=True)

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

outputs = model(**inputs)
```
{"language": "en", "tags": "autotrain", "datasets": ["crcb/autotrain-data-go_emo"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 31.11935827749309}
transformers pytorch bert text-classification autotrain en dataset:crcb/autotrain-data-go_emo co2_eq_emissions autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #text-classification #autotrain #en #dataset-crcb/autotrain-data-go_emo #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 


# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 748922872
- CO2 Emissions (in grams): 31.11935827749309

## Validation Metrics

- Loss: 0.17039568722248077
- Accuracy: 0.93625
- Macro F1: 0.9075787460059076
- Micro F1: 0.93625
- Weighted F1: 0.9371621543264445
- Macro Precision: 0.8945117620407296
- Micro Precision: 0.93625
- Weighted Precision: 0.9433589433926076
- Macro Recall: 0.9323604226458176
- Micro Recall: 0.93625
- Weighted Recall: 0.93625


## Usage

You can use cURL to access this model:



Or Python API:


# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 748922872
- CO2 Emissions (in grams): 31.11935827749309 ## Validation Metrics

- Loss: 0.17039568722248077
- Accuracy: 0.93625
- Macro F1: 0.9075787460059076
- Micro F1: 0.93625
- Weighted F1: 0.9371621543264445
- Macro Precision: 0.8945117620407296
- Micro Precision: 0.93625
- Weighted Precision: 0.9433589433926076
- Macro Recall: 0.9323604226458176
- Micro Recall: 0.93625
- Weighted Recall: 0.93625 ## Usage

You can use cURL to access this model:



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

- Problem type: Multi-class Classification
- Model ID: 748922872
- CO2 Emissions (in grams): 31.11935827749309 ## Validation Metrics

- Loss: 0.17039568722248077
- Accuracy: 0.93625
- Macro F1: 0.9075787460059076
- Micro F1: 0.93625
- Weighted F1: 0.9371621543264445
- Macro Precision: 0.8945117620407296
- Micro Precision: 0.93625
- Weighted Precision: 0.9433589433926076
- Macro Recall: 0.9323604226458176
- Micro Recall: 0.93625
- Weighted Recall: 0.93625 ## 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-go_emo #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 748922872
- CO2 Emissions (in grams): 31.11935827749309## Validation Metrics

- Loss: 0.17039568722248077
- Accuracy: 0.93625
- Macro F1: 0.9075787460059076
- Micro F1: 0.93625
- Weighted F1: 0.9371621543264445
- Macro Precision: 0.8945117620407296
- Micro Precision: 0.93625
- Weighted Precision: 0.9433589433926076
- Macro Recall: 0.9323604226458176
- Micro Recall: 0.93625
- Weighted Recall: 0.93625## Usage

You can use cURL to access this model:



Or Python API: