
# Model Trained Using AutoNLP

- Problem type: Binary Classification
- Model ID: 36789092
- CO2 Emissions (in grams): 1.4280361775467445

## Validation Metrics

- Loss: 0.5255328416824341
- Accuracy: 0.7666078777189889
- Precision: 0.6913123844731978
- Recall: 0.6192052980132451
- AUC: 0.7893359070795125
- F1: 0.6532751091703057

## 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/am4nsolanki/autonlp-text-hateful-memes-36789092
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("am4nsolanki/autonlp-text-hateful-memes-36789092", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("am4nsolanki/autonlp-text-hateful-memes-36789092", use_auth_token=True)

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

outputs = model(**inputs)
```
{"language": "en", "tags": "autonlp", "datasets": ["am4nsolanki/autonlp-data-text-hateful-memes"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 1.4280361775467445}
transformers pytorch distilbert text-classification autonlp en dataset:am4nsolanki/autonlp-data-text-hateful-memes co2_eq_emissions autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #distilbert #text-classification #autonlp #en #dataset-am4nsolanki/autonlp-data-text-hateful-memes #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 


# Model Trained Using AutoNLP

- Problem type: Binary Classification
- Model ID: 36789092
- CO2 Emissions (in grams): 1.4280361775467445

## Validation Metrics

- Loss: 0.5255328416824341
- Accuracy: 0.7666078777189889
- Precision: 0.6913123844731978
- Recall: 0.6192052980132451
- AUC: 0.7893359070795125
- F1: 0.6532751091703057

## Usage

You can use cURL to access this model:



Or Python API:


# Model Trained Using AutoNLP

- Problem type: Binary Classification
- Model ID: 36789092
- CO2 Emissions (in grams): 1.4280361775467445 ## Validation Metrics

- Loss: 0.5255328416824341
- Accuracy: 0.7666078777189889
- Precision: 0.6913123844731978
- Recall: 0.6192052980132451
- AUC: 0.7893359070795125
- F1: 0.6532751091703057 ## Usage

You can use cURL to access this model:



Or Python API:
TAGS
#transformers #pytorch #distilbert #text-classification #autonlp #en #dataset-am4nsolanki/autonlp-data-text-hateful-memes #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
 # Model Trained Using AutoNLP

- Problem type: Binary Classification
- Model ID: 36789092
- CO2 Emissions (in grams): 1.4280361775467445 ## Validation Metrics

- Loss: 0.5255328416824341
- Accuracy: 0.7666078777189889
- Precision: 0.6913123844731978
- Recall: 0.6192052980132451
- AUC: 0.7893359070795125
- F1: 0.6532751091703057 ## Usage

You can use cURL to access this model:



Or Python API:
passage: TAGS
#transformers #pytorch #distilbert #text-classification #autonlp #en #dataset-am4nsolanki/autonlp-data-text-hateful-memes #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
# Model Trained Using AutoNLP

- Problem type: Binary Classification
- Model ID: 36789092
- CO2 Emissions (in grams): 1.4280361775467445## Validation Metrics

- Loss: 0.5255328416824341
- Accuracy: 0.7666078777189889
- Precision: 0.6913123844731978
- Recall: 0.6192052980132451
- AUC: 0.7893359070795125
- F1: 0.6532751091703057## Usage

You can use cURL to access this model:



Or Python API: