
# Model Trained Using AutoNLP

- Problem type: Multi-class Classification
- Model ID: 529214890
- CO2 Emissions (in grams): 49.618294309910624

## Validation Metrics

- Loss: 0.7135734558105469
- Accuracy: 0.7042338838232481
- Macro F1: 0.6164041045783032
- Micro F1: 0.7042338838232481
- Weighted F1: 0.7028309161791009
- Macro Precision: 0.6497438111060598
- Micro Precision: 0.7042338838232481
- Weighted Precision: 0.7076651075198755
- Macro Recall: 0.6023419083862918
- Micro Recall: 0.7042338838232481
- Weighted Recall: 0.7042338838232481


## 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/lucianpopa/autonlp-SST1-529214890
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("lucianpopa/autonlp-SST1-529214890", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("lucianpopa/autonlp-SST1-529214890", use_auth_token=True)

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

outputs = model(**inputs)
```
{"language": "en", "tags": "autonlp", "datasets": ["lucianpopa/autonlp-data-SST1"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 49.618294309910624}
transformers pytorch roberta text-classification autonlp en dataset:lucianpopa/autonlp-data-SST1 co2_eq_emissions autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #roberta #text-classification #autonlp #en #dataset-lucianpopa/autonlp-data-SST1 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 


# Model Trained Using AutoNLP

- Problem type: Multi-class Classification
- Model ID: 529214890
- CO2 Emissions (in grams): 49.618294309910624

## Validation Metrics

- Loss: 0.7135734558105469
- Accuracy: 0.7042338838232481
- Macro F1: 0.6164041045783032
- Micro F1: 0.7042338838232481
- Weighted F1: 0.7028309161791009
- Macro Precision: 0.6497438111060598
- Micro Precision: 0.7042338838232481
- Weighted Precision: 0.7076651075198755
- Macro Recall: 0.6023419083862918
- Micro Recall: 0.7042338838232481
- Weighted Recall: 0.7042338838232481


## Usage

You can use cURL to access this model:



Or Python API:


# Model Trained Using AutoNLP

- Problem type: Multi-class Classification
- Model ID: 529214890
- CO2 Emissions (in grams): 49.618294309910624 ## Validation Metrics

- Loss: 0.7135734558105469
- Accuracy: 0.7042338838232481
- Macro F1: 0.6164041045783032
- Micro F1: 0.7042338838232481
- Weighted F1: 0.7028309161791009
- Macro Precision: 0.6497438111060598
- Micro Precision: 0.7042338838232481
- Weighted Precision: 0.7076651075198755
- Macro Recall: 0.6023419083862918
- Micro Recall: 0.7042338838232481
- Weighted Recall: 0.7042338838232481 ## Usage

You can use cURL to access this model:



Or Python API:
TAGS
#transformers #pytorch #roberta #text-classification #autonlp #en #dataset-lucianpopa/autonlp-data-SST1 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
 # Model Trained Using AutoNLP

- Problem type: Multi-class Classification
- Model ID: 529214890
- CO2 Emissions (in grams): 49.618294309910624 ## Validation Metrics

- Loss: 0.7135734558105469
- Accuracy: 0.7042338838232481
- Macro F1: 0.6164041045783032
- Micro F1: 0.7042338838232481
- Weighted F1: 0.7028309161791009
- Macro Precision: 0.6497438111060598
- Micro Precision: 0.7042338838232481
- Weighted Precision: 0.7076651075198755
- Macro Recall: 0.6023419083862918
- Micro Recall: 0.7042338838232481
- Weighted Recall: 0.7042338838232481 ## Usage

You can use cURL to access this model:



Or Python API:
passage: TAGS
#transformers #pytorch #roberta #text-classification #autonlp #en #dataset-lucianpopa/autonlp-data-SST1 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
# Model Trained Using AutoNLP

- Problem type: Multi-class Classification
- Model ID: 529214890
- CO2 Emissions (in grams): 49.618294309910624## Validation Metrics

- Loss: 0.7135734558105469
- Accuracy: 0.7042338838232481
- Macro F1: 0.6164041045783032
- Micro F1: 0.7042338838232481
- Weighted F1: 0.7028309161791009
- Macro Precision: 0.6497438111060598
- Micro Precision: 0.7042338838232481
- Weighted Precision: 0.7076651075198755
- Macro Recall: 0.6023419083862918
- Micro Recall: 0.7042338838232481
- Weighted Recall: 0.7042338838232481## Usage

You can use cURL to access this model:



Or Python API: