
# Model Trained Using AutoNLP

- Problem type: Multi-class Classification
- Model ID: 631818261
- CO2 Emissions (in grams): 5.714250590300453

## Validation Metrics

- Loss: 0.44651690125465393
- Accuracy: 0.8792873051224944
- Macro F1: 0.839261602941426
- Micro F1: 0.8792873051224943
- Weighted F1: 0.8790427387522044
- Macro Precision: 0.8407634723656228
- Micro Precision: 0.8792873051224944
- Weighted Precision: 0.8801219917819031
- Macro Recall: 0.8400328140795883
- Micro Recall: 0.8792873051224944
- Weighted Recall: 0.8792873051224944


## 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 AutoNLP"}' https://api-inference.huggingface.co/models/test1345/autonlp-savesome-631818261
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("test1345/autonlp-savesome-631818261", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("test1345/autonlp-savesome-631818261", use_auth_token=True)

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

outputs = model(**inputs)
```
{"language": "unk", "tags": "autonlp", "datasets": ["test1345/autonlp-data-savesome"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 5.714250590300453}
transformers pytorch roberta text-classification autonlp unk dataset:test1345/autonlp-data-savesome co2_eq_emissions autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #roberta #text-classification #autonlp #unk #dataset-test1345/autonlp-data-savesome #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 


# Model Trained Using AutoNLP

- Problem type: Multi-class Classification
- Model ID: 631818261
- CO2 Emissions (in grams): 5.714250590300453

## Validation Metrics

- Loss: 0.44651690125465393
- Accuracy: 0.8792873051224944
- Macro F1: 0.839261602941426
- Micro F1: 0.8792873051224943
- Weighted F1: 0.8790427387522044
- Macro Precision: 0.8407634723656228
- Micro Precision: 0.8792873051224944
- Weighted Precision: 0.8801219917819031
- Macro Recall: 0.8400328140795883
- Micro Recall: 0.8792873051224944
- Weighted Recall: 0.8792873051224944


## Usage

You can use cURL to access this model:



Or Python API:


# Model Trained Using AutoNLP

- Problem type: Multi-class Classification
- Model ID: 631818261
- CO2 Emissions (in grams): 5.714250590300453 ## Validation Metrics

- Loss: 0.44651690125465393
- Accuracy: 0.8792873051224944
- Macro F1: 0.839261602941426
- Micro F1: 0.8792873051224943
- Weighted F1: 0.8790427387522044
- Macro Precision: 0.8407634723656228
- Micro Precision: 0.8792873051224944
- Weighted Precision: 0.8801219917819031
- Macro Recall: 0.8400328140795883
- Micro Recall: 0.8792873051224944
- Weighted Recall: 0.8792873051224944 ## Usage

You can use cURL to access this model:



Or Python API:
TAGS
#transformers #pytorch #roberta #text-classification #autonlp #unk #dataset-test1345/autonlp-data-savesome #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
 # Model Trained Using AutoNLP

- Problem type: Multi-class Classification
- Model ID: 631818261
- CO2 Emissions (in grams): 5.714250590300453 ## Validation Metrics

- Loss: 0.44651690125465393
- Accuracy: 0.8792873051224944
- Macro F1: 0.839261602941426
- Micro F1: 0.8792873051224943
- Weighted F1: 0.8790427387522044
- Macro Precision: 0.8407634723656228
- Micro Precision: 0.8792873051224944
- Weighted Precision: 0.8801219917819031
- Macro Recall: 0.8400328140795883
- Micro Recall: 0.8792873051224944
- Weighted Recall: 0.8792873051224944 ## Usage

You can use cURL to access this model:



Or Python API:
passage: TAGS
#transformers #pytorch #roberta #text-classification #autonlp #unk #dataset-test1345/autonlp-data-savesome #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
# Model Trained Using AutoNLP

- Problem type: Multi-class Classification
- Model ID: 631818261
- CO2 Emissions (in grams): 5.714250590300453## Validation Metrics

- Loss: 0.44651690125465393
- Accuracy: 0.8792873051224944
- Macro F1: 0.839261602941426
- Micro F1: 0.8792873051224943
- Weighted F1: 0.8790427387522044
- Macro Precision: 0.8407634723656228
- Micro Precision: 0.8792873051224944
- Weighted Precision: 0.8801219917819031
- Macro Recall: 0.8400328140795883
- Micro Recall: 0.8792873051224944
- Weighted Recall: 0.8792873051224944## Usage

You can use cURL to access this model:



Or Python API: