
# Model Trained Using AutoNLP

- Problem type: Multi-class Classification
- Model ID: 640218740
- CO2 Emissions (in grams): 43.078469852595994

## Validation Metrics

- Loss: 0.8302136063575745
- Accuracy: 0.7887341933835739
- Macro F1: 0.5756730305293746
- Micro F1: 0.7887341933835739
- Weighted F1: 0.7878942570915727
- Macro Precision: 0.620883634472996
- Micro Precision: 0.7887341933835739
- Weighted Precision: 0.8009430092038783
- Macro Recall: 0.5521761315904072
- Micro Recall: 0.7887341933835739
- Weighted Recall: 0.7887341933835739


## 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/gabitoo1234/autonlp-mut_uchile-640218740
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("gabitoo1234/autonlp-mut_uchile-640218740", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("gabitoo1234/autonlp-mut_uchile-640218740", use_auth_token=True)

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

outputs = model(**inputs)
```
{"language": "unk", "tags": "autonlp", "datasets": ["gabitoo1234/autonlp-data-mut_uchile"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 43.078469852595994}
transformers pytorch bert text-classification autonlp unk dataset:gabitoo1234/autonlp-data-mut_uchile co2_eq_emissions autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #text-classification #autonlp #unk #dataset-gabitoo1234/autonlp-data-mut_uchile #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 


# Model Trained Using AutoNLP

- Problem type: Multi-class Classification
- Model ID: 640218740
- CO2 Emissions (in grams): 43.078469852595994

## Validation Metrics

- Loss: 0.8302136063575745
- Accuracy: 0.7887341933835739
- Macro F1: 0.5756730305293746
- Micro F1: 0.7887341933835739
- Weighted F1: 0.7878942570915727
- Macro Precision: 0.620883634472996
- Micro Precision: 0.7887341933835739
- Weighted Precision: 0.8009430092038783
- Macro Recall: 0.5521761315904072
- Micro Recall: 0.7887341933835739
- Weighted Recall: 0.7887341933835739


## Usage

You can use cURL to access this model:



Or Python API:


# Model Trained Using AutoNLP

- Problem type: Multi-class Classification
- Model ID: 640218740
- CO2 Emissions (in grams): 43.078469852595994 ## Validation Metrics

- Loss: 0.8302136063575745
- Accuracy: 0.7887341933835739
- Macro F1: 0.5756730305293746
- Micro F1: 0.7887341933835739
- Weighted F1: 0.7878942570915727
- Macro Precision: 0.620883634472996
- Micro Precision: 0.7887341933835739
- Weighted Precision: 0.8009430092038783
- Macro Recall: 0.5521761315904072
- Micro Recall: 0.7887341933835739
- Weighted Recall: 0.7887341933835739 ## Usage

You can use cURL to access this model:



Or Python API:
TAGS
#transformers #pytorch #bert #text-classification #autonlp #unk #dataset-gabitoo1234/autonlp-data-mut_uchile #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
 # Model Trained Using AutoNLP

- Problem type: Multi-class Classification
- Model ID: 640218740
- CO2 Emissions (in grams): 43.078469852595994 ## Validation Metrics

- Loss: 0.8302136063575745
- Accuracy: 0.7887341933835739
- Macro F1: 0.5756730305293746
- Micro F1: 0.7887341933835739
- Weighted F1: 0.7878942570915727
- Macro Precision: 0.620883634472996
- Micro Precision: 0.7887341933835739
- Weighted Precision: 0.8009430092038783
- Macro Recall: 0.5521761315904072
- Micro Recall: 0.7887341933835739
- Weighted Recall: 0.7887341933835739 ## Usage

You can use cURL to access this model:



Or Python API:
passage: TAGS
#transformers #pytorch #bert #text-classification #autonlp #unk #dataset-gabitoo1234/autonlp-data-mut_uchile #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
# Model Trained Using AutoNLP

- Problem type: Multi-class Classification
- Model ID: 640218740
- CO2 Emissions (in grams): 43.078469852595994## Validation Metrics

- Loss: 0.8302136063575745
- Accuracy: 0.7887341933835739
- Macro F1: 0.5756730305293746
- Micro F1: 0.7887341933835739
- Weighted F1: 0.7878942570915727
- Macro Precision: 0.620883634472996
- Micro Precision: 0.7887341933835739
- Weighted Precision: 0.8009430092038783
- Macro Recall: 0.5521761315904072
- Micro Recall: 0.7887341933835739
- Weighted Recall: 0.7887341933835739## Usage

You can use cURL to access this model:



Or Python API: