
# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 1055036381
- CO2 Emissions (in grams): 17.43982800509071

## Validation Metrics

- Loss: 0.6177256107330322
- Accuracy: 0.7306006137658921
- Macro F1: 0.719534854339415
- Micro F1: 0.730600613765892
- Weighted F1: 0.7302204676842725
- Macro Precision: 0.714938066281146
- Micro Precision: 0.7306006137658921
- Weighted Precision: 0.7316651970219867
- Macro Recall: 0.7258484087500343
- Micro Recall: 0.7306006137658921
- Weighted Recall: 0.7306006137658921


## 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/kakashi210/autotrain-tweet-sentiment-classifier-1055036381
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("kakashi210/autotrain-tweet-sentiment-classifier-1055036381", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("kakashi210/autotrain-tweet-sentiment-classifier-1055036381", use_auth_token=True)

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

outputs = model(**inputs)
```
{"language": "unk", "tags": "autotrain", "datasets": ["kakashi210/autotrain-data-tweet-sentiment-classifier"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 17.43982800509071}
transformers pytorch distilbert text-classification autotrain unk dataset:kakashi210/autotrain-data-tweet-sentiment-classifier co2_eq_emissions autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #distilbert #text-classification #autotrain #unk #dataset-kakashi210/autotrain-data-tweet-sentiment-classifier #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 


# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 1055036381
- CO2 Emissions (in grams): 17.43982800509071

## Validation Metrics

- Loss: 0.6177256107330322
- Accuracy: 0.7306006137658921
- Macro F1: 0.719534854339415
- Micro F1: 0.730600613765892
- Weighted F1: 0.7302204676842725
- Macro Precision: 0.714938066281146
- Micro Precision: 0.7306006137658921
- Weighted Precision: 0.7316651970219867
- Macro Recall: 0.7258484087500343
- Micro Recall: 0.7306006137658921
- Weighted Recall: 0.7306006137658921


## Usage

You can use cURL to access this model:



Or Python API:


# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 1055036381
- CO2 Emissions (in grams): 17.43982800509071 ## Validation Metrics

- Loss: 0.6177256107330322
- Accuracy: 0.7306006137658921
- Macro F1: 0.719534854339415
- Micro F1: 0.730600613765892
- Weighted F1: 0.7302204676842725
- Macro Precision: 0.714938066281146
- Micro Precision: 0.7306006137658921
- Weighted Precision: 0.7316651970219867
- Macro Recall: 0.7258484087500343
- Micro Recall: 0.7306006137658921
- Weighted Recall: 0.7306006137658921 ## Usage

You can use cURL to access this model:



Or Python API:
TAGS
#transformers #pytorch #distilbert #text-classification #autotrain #unk #dataset-kakashi210/autotrain-data-tweet-sentiment-classifier #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
 # Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 1055036381
- CO2 Emissions (in grams): 17.43982800509071 ## Validation Metrics

- Loss: 0.6177256107330322
- Accuracy: 0.7306006137658921
- Macro F1: 0.719534854339415
- Micro F1: 0.730600613765892
- Weighted F1: 0.7302204676842725
- Macro Precision: 0.714938066281146
- Micro Precision: 0.7306006137658921
- Weighted Precision: 0.7316651970219867
- Macro Recall: 0.7258484087500343
- Micro Recall: 0.7306006137658921
- Weighted Recall: 0.7306006137658921 ## Usage

You can use cURL to access this model:



Or Python API:
passage: TAGS
#transformers #pytorch #distilbert #text-classification #autotrain #unk #dataset-kakashi210/autotrain-data-tweet-sentiment-classifier #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 1055036381
- CO2 Emissions (in grams): 17.43982800509071## Validation Metrics

- Loss: 0.6177256107330322
- Accuracy: 0.7306006137658921
- Macro F1: 0.719534854339415
- Micro F1: 0.730600613765892
- Weighted F1: 0.7302204676842725
- Macro Precision: 0.714938066281146
- Micro Precision: 0.7306006137658921
- Weighted Precision: 0.7316651970219867
- Macro Recall: 0.7258484087500343
- Micro Recall: 0.7306006137658921
- Weighted Recall: 0.7306006137658921## Usage

You can use cURL to access this model:



Or Python API: