
# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 1281148992
- CO2 Emissions (in grams): 10.6761

## Validation Metrics

- Loss: 0.606
- Accuracy: 0.728
- Macro F1: 0.710
- Micro F1: 0.728
- Weighted F1: 0.728
- Macro Precision: 0.716
- Micro Precision: 0.728
- Weighted Precision: 0.729
- Macro Recall: 0.706
- Micro Recall: 0.728
- Weighted Recall: 0.728


## 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/sasha/autotrain-DistilBERT-TweetEval-1281148992
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("sasha/autotrain-DistilBERT-TweetEval-1281148992", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("sasha/autotrain-DistilBERT-TweetEval-1281148992", use_auth_token=True)

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

outputs = model(**inputs)
```
{"language": ["unk"], "tags": ["autotrain", "text-classification"], "datasets": ["sasha/autotrain-data-DistilBERT-TweetEval"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": {"emissions": 10.676055974144631}}
transformers pytorch distilbert text-classification autotrain unk dataset:sasha/autotrain-data-DistilBERT-TweetEval co2_eq_emissions autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #distilbert #text-classification #autotrain #unk #dataset-sasha/autotrain-data-DistilBERT-TweetEval #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 


# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 1281148992
- CO2 Emissions (in grams): 10.6761

## Validation Metrics

- Loss: 0.606
- Accuracy: 0.728
- Macro F1: 0.710
- Micro F1: 0.728
- Weighted F1: 0.728
- Macro Precision: 0.716
- Micro Precision: 0.728
- Weighted Precision: 0.729
- Macro Recall: 0.706
- Micro Recall: 0.728
- Weighted Recall: 0.728


## Usage

You can use cURL to access this model:



Or Python API:


# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 1281148992
- CO2 Emissions (in grams): 10.6761 ## Validation Metrics

- Loss: 0.606
- Accuracy: 0.728
- Macro F1: 0.710
- Micro F1: 0.728
- Weighted F1: 0.728
- Macro Precision: 0.716
- Micro Precision: 0.728
- Weighted Precision: 0.729
- Macro Recall: 0.706
- Micro Recall: 0.728
- Weighted Recall: 0.728 ## Usage

You can use cURL to access this model:



Or Python API:
TAGS
#transformers #pytorch #distilbert #text-classification #autotrain #unk #dataset-sasha/autotrain-data-DistilBERT-TweetEval #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
 # Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 1281148992
- CO2 Emissions (in grams): 10.6761 ## Validation Metrics

- Loss: 0.606
- Accuracy: 0.728
- Macro F1: 0.710
- Micro F1: 0.728
- Weighted F1: 0.728
- Macro Precision: 0.716
- Micro Precision: 0.728
- Weighted Precision: 0.729
- Macro Recall: 0.706
- Micro Recall: 0.728
- Weighted Recall: 0.728 ## Usage

You can use cURL to access this model:



Or Python API:
TAGS
#transformers #pytorch #distilbert #text-classification #autotrain #unk #dataset-sasha/autotrain-data-DistilBERT-TweetEval #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 1281148992
- CO2 Emissions (in grams): 10.6761## Validation Metrics

- Loss: 0.606
- Accuracy: 0.728
- Macro F1: 0.710
- Micro F1: 0.728
- Weighted F1: 0.728
- Macro Precision: 0.716
- Micro Precision: 0.728
- Weighted Precision: 0.729
- Macro Recall: 0.706
- Micro Recall: 0.728
- Weighted Recall: 0.728## Usage

You can use cURL to access this model:



Or Python API: