
# Model Trained Using AutoTrain

- Problem type: Binary Classification
- Model ID: 1034235509
- CO2 Emissions (in grams): 17.051424016530056

## Validation Metrics

- Loss: 0.14414940774440765
- Accuracy: 0.954046028210839
- Precision: 0.9583831937242387
- Recall: 0.9592760180995475
- AUC: 0.9872623710421541
- F1: 0.9588293980711673

## 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/deepesh0x/autotrain-bert_wikipedia_sst_2-1034235509
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("deepesh0x/autotrain-bert_wikipedia_sst_2-1034235509", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("deepesh0x/autotrain-bert_wikipedia_sst_2-1034235509", use_auth_token=True)

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

outputs = model(**inputs)
```
{"language": "unk", "tags": "autotrain", "datasets": ["deepesh0x/autotrain-data-bert_wikipedia_sst_2"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 17.051424016530056}
transformers pytorch bert text-classification autotrain unk dataset:deepesh0x/autotrain-data-bert_wikipedia_sst_2 co2_eq_emissions autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #text-classification #autotrain #unk #dataset-deepesh0x/autotrain-data-bert_wikipedia_sst_2 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 


# Model Trained Using AutoTrain

- Problem type: Binary Classification
- Model ID: 1034235509
- CO2 Emissions (in grams): 17.051424016530056

## Validation Metrics

- Loss: 0.14414940774440765
- Accuracy: 0.954046028210839
- Precision: 0.9583831937242387
- Recall: 0.9592760180995475
- AUC: 0.9872623710421541
- F1: 0.9588293980711673

## Usage

You can use cURL to access this model:



Or Python API:


# Model Trained Using AutoTrain

- Problem type: Binary Classification
- Model ID: 1034235509
- CO2 Emissions (in grams): 17.051424016530056 ## Validation Metrics

- Loss: 0.14414940774440765
- Accuracy: 0.954046028210839
- Precision: 0.9583831937242387
- Recall: 0.9592760180995475
- AUC: 0.9872623710421541
- F1: 0.9588293980711673 ## Usage

You can use cURL to access this model:



Or Python API:
TAGS
#transformers #pytorch #bert #text-classification #autotrain #unk #dataset-deepesh0x/autotrain-data-bert_wikipedia_sst_2 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
 # Model Trained Using AutoTrain

- Problem type: Binary Classification
- Model ID: 1034235509
- CO2 Emissions (in grams): 17.051424016530056 ## Validation Metrics

- Loss: 0.14414940774440765
- Accuracy: 0.954046028210839
- Precision: 0.9583831937242387
- Recall: 0.9592760180995475
- AUC: 0.9872623710421541
- F1: 0.9588293980711673 ## Usage

You can use cURL to access this model:



Or Python API:
passage: TAGS
#transformers #pytorch #bert #text-classification #autotrain #unk #dataset-deepesh0x/autotrain-data-bert_wikipedia_sst_2 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
# Model Trained Using AutoTrain

- Problem type: Binary Classification
- Model ID: 1034235509
- CO2 Emissions (in grams): 17.051424016530056## Validation Metrics

- Loss: 0.14414940774440765
- Accuracy: 0.954046028210839
- Precision: 0.9583831937242387
- Recall: 0.9592760180995475
- AUC: 0.9872623710421541
- F1: 0.9588293980711673## Usage

You can use cURL to access this model:



Or Python API: