
# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 813325491
- CO2 Emissions (in grams): 20.58663910106142

## Validation Metrics

- Loss: 1.3628994226455688
- Accuracy: 0.5920355494787216
- Macro F1: 0.4844439507523978
- Micro F1: 0.5920355494787216
- Weighted F1: 0.5873137663478112
- Macro Precision: 0.5458988948121151
- Micro Precision: 0.5920355494787216
- Weighted Precision: 0.591386299522425
- Macro Recall: 0.4753100798358001
- Micro Recall: 0.5920355494787216
- Weighted Recall: 0.5920355494787216


## 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_new-813325491
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("crcb/autotrain-go_emo_new-813325491", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("crcb/autotrain-go_emo_new-813325491", use_auth_token=True)

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

outputs = model(**inputs)
```
{"language": "unk", "tags": "autotrain", "datasets": ["crcb/autotrain-data-go_emo_new"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 20.58663910106142}
transformers pytorch roberta text-classification autotrain unk dataset:crcb/autotrain-data-go_emo_new co2_eq_emissions autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #roberta #text-classification #autotrain #unk #dataset-crcb/autotrain-data-go_emo_new #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 


# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 813325491
- CO2 Emissions (in grams): 20.58663910106142

## Validation Metrics

- Loss: 1.3628994226455688
- Accuracy: 0.5920355494787216
- Macro F1: 0.4844439507523978
- Micro F1: 0.5920355494787216
- Weighted F1: 0.5873137663478112
- Macro Precision: 0.5458988948121151
- Micro Precision: 0.5920355494787216
- Weighted Precision: 0.591386299522425
- Macro Recall: 0.4753100798358001
- Micro Recall: 0.5920355494787216
- Weighted Recall: 0.5920355494787216


## Usage

You can use cURL to access this model:



Or Python API:


# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 813325491
- CO2 Emissions (in grams): 20.58663910106142 ## Validation Metrics

- Loss: 1.3628994226455688
- Accuracy: 0.5920355494787216
- Macro F1: 0.4844439507523978
- Micro F1: 0.5920355494787216
- Weighted F1: 0.5873137663478112
- Macro Precision: 0.5458988948121151
- Micro Precision: 0.5920355494787216
- Weighted Precision: 0.591386299522425
- Macro Recall: 0.4753100798358001
- Micro Recall: 0.5920355494787216
- Weighted Recall: 0.5920355494787216 ## Usage

You can use cURL to access this model:



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

- Problem type: Multi-class Classification
- Model ID: 813325491
- CO2 Emissions (in grams): 20.58663910106142 ## Validation Metrics

- Loss: 1.3628994226455688
- Accuracy: 0.5920355494787216
- Macro F1: 0.4844439507523978
- Micro F1: 0.5920355494787216
- Weighted F1: 0.5873137663478112
- Macro Precision: 0.5458988948121151
- Micro Precision: 0.5920355494787216
- Weighted Precision: 0.591386299522425
- Macro Recall: 0.4753100798358001
- Micro Recall: 0.5920355494787216
- Weighted Recall: 0.5920355494787216 ## Usage

You can use cURL to access this model:



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

- Problem type: Multi-class Classification
- Model ID: 813325491
- CO2 Emissions (in grams): 20.58663910106142## Validation Metrics

- Loss: 1.3628994226455688
- Accuracy: 0.5920355494787216
- Macro F1: 0.4844439507523978
- Micro F1: 0.5920355494787216
- Weighted F1: 0.5873137663478112
- Macro Precision: 0.5458988948121151
- Micro Precision: 0.5920355494787216
- Weighted Precision: 0.591386299522425
- Macro Recall: 0.4753100798358001
- Micro Recall: 0.5920355494787216
- Weighted Recall: 0.5920355494787216## Usage

You can use cURL to access this model:



Or Python API: