
# Model Trained Using AutoNLP

- Problem type: Multi-class Classification
- Model ID: 10022181

## Validation Metrics

- Loss: 0.369505375623703
- Accuracy: 0.8706206896551724
- Macro F1: 0.5410226656476808
- Micro F1: 0.8706206896551724
- Weighted F1: 0.8515634683886795
- Macro Precision: 0.5159711665622992
- Micro Precision: 0.8706206896551724
- Weighted Precision: 0.8346991124101657
- Macro Recall: 0.5711653346601209
- Micro Recall: 0.8706206896551724
- Weighted Recall: 0.8706206896551724


## 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/bshlgrs/autonlp-old-data-trained-10022181
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("bshlgrs/autonlp-old-data-trained-10022181", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("bshlgrs/autonlp-old-data-trained-10022181", use_auth_token=True)

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

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


# Model Trained Using AutoNLP

- Problem type: Multi-class Classification
- Model ID: 10022181

## Validation Metrics

- Loss: 0.369505375623703
- Accuracy: 0.8706206896551724
- Macro F1: 0.5410226656476808
- Micro F1: 0.8706206896551724
- Weighted F1: 0.8515634683886795
- Macro Precision: 0.5159711665622992
- Micro Precision: 0.8706206896551724
- Weighted Precision: 0.8346991124101657
- Macro Recall: 0.5711653346601209
- Micro Recall: 0.8706206896551724
- Weighted Recall: 0.8706206896551724


## Usage

You can use cURL to access this model:



Or Python API:


# Model Trained Using AutoNLP

- Problem type: Multi-class Classification
- Model ID: 10022181 ## Validation Metrics

- Loss: 0.369505375623703
- Accuracy: 0.8706206896551724
- Macro F1: 0.5410226656476808
- Micro F1: 0.8706206896551724
- Weighted F1: 0.8515634683886795
- Macro Precision: 0.5159711665622992
- Micro Precision: 0.8706206896551724
- Weighted Precision: 0.8346991124101657
- Macro Recall: 0.5711653346601209
- Micro Recall: 0.8706206896551724
- Weighted Recall: 0.8706206896551724 ## Usage

You can use cURL to access this model:



Or Python API:
TAGS
#transformers #pytorch #bert #text-classification #autonlp #en #dataset-bshlgrs/autonlp-data-old-data-trained #autotrain_compatible #endpoints_compatible #region-us 
 # Model Trained Using AutoNLP

- Problem type: Multi-class Classification
- Model ID: 10022181 ## Validation Metrics

- Loss: 0.369505375623703
- Accuracy: 0.8706206896551724
- Macro F1: 0.5410226656476808
- Micro F1: 0.8706206896551724
- Weighted F1: 0.8515634683886795
- Macro Precision: 0.5159711665622992
- Micro Precision: 0.8706206896551724
- Weighted Precision: 0.8346991124101657
- Macro Recall: 0.5711653346601209
- Micro Recall: 0.8706206896551724
- Weighted Recall: 0.8706206896551724 ## Usage

You can use cURL to access this model:



Or Python API:
passage: TAGS
#transformers #pytorch #bert #text-classification #autonlp #en #dataset-bshlgrs/autonlp-data-old-data-trained #autotrain_compatible #endpoints_compatible #region-us 
# Model Trained Using AutoNLP

- Problem type: Multi-class Classification
- Model ID: 10022181## Validation Metrics

- Loss: 0.369505375623703
- Accuracy: 0.8706206896551724
- Macro F1: 0.5410226656476808
- Micro F1: 0.8706206896551724
- Weighted F1: 0.8515634683886795
- Macro Precision: 0.5159711665622992
- Micro Precision: 0.8706206896551724
- Weighted Precision: 0.8346991124101657
- Macro Recall: 0.5711653346601209
- Micro Recall: 0.8706206896551724
- Weighted Recall: 0.8706206896551724## Usage

You can use cURL to access this model:



Or Python API: