
# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 754123133
- CO2 Emissions (in grams): 0.005300030853867218

## Validation Metrics

- Loss: 0.387116938829422
- Accuracy: 0.8658536585365854
- Macro F1: 0.7724053724053724
- Micro F1: 0.8658536585365854
- Weighted F1: 0.8467166979362101
- Macro Precision: 0.8232219717155155
- Micro Precision: 0.8658536585365854
- Weighted Precision: 0.8516026874759421
- Macro Recall: 0.7642089093701996
- Micro Recall: 0.8658536585365854
- Weighted Recall: 0.8658536585365854


## 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/zainalq7/autotrain-NLU_crypto_sentiment_analysis-754123133
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("zainalq7/autotrain-NLU_crypto_sentiment_analysis-754123133", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("zainalq7/autotrain-NLU_crypto_sentiment_analysis-754123133", use_auth_token=True)

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

outputs = model(**inputs)
```
{"language": "unk", "tags": "autotrain", "datasets": ["zainalq7/autotrain-data-NLU_crypto_sentiment_analysis"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 0.005300030853867218}
transformers pytorch roberta text-classification autotrain unk dataset:zainalq7/autotrain-data-NLU_crypto_sentiment_analysis co2_eq_emissions autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #roberta #text-classification #autotrain #unk #dataset-zainalq7/autotrain-data-NLU_crypto_sentiment_analysis #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 


# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 754123133
- CO2 Emissions (in grams): 0.005300030853867218

## Validation Metrics

- Loss: 0.387116938829422
- Accuracy: 0.8658536585365854
- Macro F1: 0.7724053724053724
- Micro F1: 0.8658536585365854
- Weighted F1: 0.8467166979362101
- Macro Precision: 0.8232219717155155
- Micro Precision: 0.8658536585365854
- Weighted Precision: 0.8516026874759421
- Macro Recall: 0.7642089093701996
- Micro Recall: 0.8658536585365854
- Weighted Recall: 0.8658536585365854


## Usage

You can use cURL to access this model:



Or Python API:


# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 754123133
- CO2 Emissions (in grams): 0.005300030853867218 ## Validation Metrics

- Loss: 0.387116938829422
- Accuracy: 0.8658536585365854
- Macro F1: 0.7724053724053724
- Micro F1: 0.8658536585365854
- Weighted F1: 0.8467166979362101
- Macro Precision: 0.8232219717155155
- Micro Precision: 0.8658536585365854
- Weighted Precision: 0.8516026874759421
- Macro Recall: 0.7642089093701996
- Micro Recall: 0.8658536585365854
- Weighted Recall: 0.8658536585365854 ## Usage

You can use cURL to access this model:



Or Python API:
TAGS
#transformers #pytorch #roberta #text-classification #autotrain #unk #dataset-zainalq7/autotrain-data-NLU_crypto_sentiment_analysis #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
 # Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 754123133
- CO2 Emissions (in grams): 0.005300030853867218 ## Validation Metrics

- Loss: 0.387116938829422
- Accuracy: 0.8658536585365854
- Macro F1: 0.7724053724053724
- Micro F1: 0.8658536585365854
- Weighted F1: 0.8467166979362101
- Macro Precision: 0.8232219717155155
- Micro Precision: 0.8658536585365854
- Weighted Precision: 0.8516026874759421
- Macro Recall: 0.7642089093701996
- Micro Recall: 0.8658536585365854
- Weighted Recall: 0.8658536585365854 ## Usage

You can use cURL to access this model:



Or Python API:
passage: TAGS
#transformers #pytorch #roberta #text-classification #autotrain #unk #dataset-zainalq7/autotrain-data-NLU_crypto_sentiment_analysis #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 754123133
- CO2 Emissions (in grams): 0.005300030853867218## Validation Metrics

- Loss: 0.387116938829422
- Accuracy: 0.8658536585365854
- Macro F1: 0.7724053724053724
- Micro F1: 0.8658536585365854
- Weighted F1: 0.8467166979362101
- Macro Precision: 0.8232219717155155
- Micro Precision: 0.8658536585365854
- Weighted Precision: 0.8516026874759421
- Macro Recall: 0.7642089093701996
- Micro Recall: 0.8658536585365854
- Weighted Recall: 0.8658536585365854## Usage

You can use cURL to access this model:



Or Python API: