
# Model Trained Using AutoNLP

- Problem type: Multi-class Classification
- Model ID: 522314623
- CO2 Emissions (in grams): 15.186006626915715

## Validation Metrics

- Loss: 0.24612033367156982
- Accuracy: 0.9643183897529735
- Macro F1: 0.9493690949638435
- Micro F1: 0.9643183897529735
- Weighted F1: 0.9642384162837268
- Macro Precision: 0.9372705571897225
- Micro Precision: 0.9643183897529735
- Weighted Precision: 0.9652870438320825
- Macro Recall: 0.9649638583139503
- Micro Recall: 0.9643183897529735
- Weighted Recall: 0.9643183897529735


## 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/lucianpopa/autonlp-TREC-classification-522314623
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("lucianpopa/autonlp-TREC-classification-522314623", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("lucianpopa/autonlp-TREC-classification-522314623", use_auth_token=True)

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

outputs = model(**inputs)
```
{"language": "en", "tags": "autonlp", "datasets": ["lucianpopa/autonlp-data-TREC-classification"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 15.186006626915715}
transformers pytorch roberta text-classification autonlp en dataset:lucianpopa/autonlp-data-TREC-classification co2_eq_emissions autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #roberta #text-classification #autonlp #en #dataset-lucianpopa/autonlp-data-TREC-classification #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 


# Model Trained Using AutoNLP

- Problem type: Multi-class Classification
- Model ID: 522314623
- CO2 Emissions (in grams): 15.186006626915715

## Validation Metrics

- Loss: 0.24612033367156982
- Accuracy: 0.9643183897529735
- Macro F1: 0.9493690949638435
- Micro F1: 0.9643183897529735
- Weighted F1: 0.9642384162837268
- Macro Precision: 0.9372705571897225
- Micro Precision: 0.9643183897529735
- Weighted Precision: 0.9652870438320825
- Macro Recall: 0.9649638583139503
- Micro Recall: 0.9643183897529735
- Weighted Recall: 0.9643183897529735


## Usage

You can use cURL to access this model:



Or Python API:


# Model Trained Using AutoNLP

- Problem type: Multi-class Classification
- Model ID: 522314623
- CO2 Emissions (in grams): 15.186006626915715 ## Validation Metrics

- Loss: 0.24612033367156982
- Accuracy: 0.9643183897529735
- Macro F1: 0.9493690949638435
- Micro F1: 0.9643183897529735
- Weighted F1: 0.9642384162837268
- Macro Precision: 0.9372705571897225
- Micro Precision: 0.9643183897529735
- Weighted Precision: 0.9652870438320825
- Macro Recall: 0.9649638583139503
- Micro Recall: 0.9643183897529735
- Weighted Recall: 0.9643183897529735 ## Usage

You can use cURL to access this model:



Or Python API:
TAGS
#transformers #pytorch #roberta #text-classification #autonlp #en #dataset-lucianpopa/autonlp-data-TREC-classification #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
 # Model Trained Using AutoNLP

- Problem type: Multi-class Classification
- Model ID: 522314623
- CO2 Emissions (in grams): 15.186006626915715 ## Validation Metrics

- Loss: 0.24612033367156982
- Accuracy: 0.9643183897529735
- Macro F1: 0.9493690949638435
- Micro F1: 0.9643183897529735
- Weighted F1: 0.9642384162837268
- Macro Precision: 0.9372705571897225
- Micro Precision: 0.9643183897529735
- Weighted Precision: 0.9652870438320825
- Macro Recall: 0.9649638583139503
- Micro Recall: 0.9643183897529735
- Weighted Recall: 0.9643183897529735 ## Usage

You can use cURL to access this model:



Or Python API:
passage: TAGS
#transformers #pytorch #roberta #text-classification #autonlp #en #dataset-lucianpopa/autonlp-data-TREC-classification #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
# Model Trained Using AutoNLP

- Problem type: Multi-class Classification
- Model ID: 522314623
- CO2 Emissions (in grams): 15.186006626915715## Validation Metrics

- Loss: 0.24612033367156982
- Accuracy: 0.9643183897529735
- Macro F1: 0.9493690949638435
- Micro F1: 0.9643183897529735
- Weighted F1: 0.9642384162837268
- Macro Precision: 0.9372705571897225
- Micro Precision: 0.9643183897529735
- Weighted Precision: 0.9652870438320825
- Macro Recall: 0.9649638583139503
- Micro Recall: 0.9643183897529735
- Weighted Recall: 0.9643183897529735## Usage

You can use cURL to access this model:



Or Python API: