
# Model Trained Using AutoTrain

- Problem type: Binary Classification
- Model ID: 737422470
- CO2 Emissions (in grams): 7.351885824089346

## Validation Metrics

- Loss: 0.39456263184547424
- Accuracy: 0.8279088689991864
- Precision: 0.6869806094182825
- Recall: 0.17663817663817663
- AUC: 0.7937892215111646
- F1: 0.2810198300283286

## 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/vabadeh213/autotrain-iine_classification10-737422470
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("vabadeh213/autotrain-iine_classification10-737422470", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("vabadeh213/autotrain-iine_classification10-737422470", use_auth_token=True)

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

outputs = model(**inputs)
```
{"language": "ja", "tags": "autotrain", "datasets": ["vabadeh213/autotrain-data-iine_classification10"], "widget": [{"text": "Rust\u3067WebAssembly\u30a4\u30f3\u30bf\u30d7\u30ea\u30bf\u3092\u4f5c\u3063\u305f\u8a71+webassembly+rust"}, {"text": "Go\u306e\u30ed\u30ae\u30f3\u30b0\u30e9\u30a4\u30d6\u30e9\u30ea 2021\u5e74\u51ac golang library logging go"}, {"text": "Vim\u3068TUI\u30c4\u30fc\u30eb\u3092\u306a\u3081\u3089\u304b\u306b\u5207\u308a\u66ff\u3048\u308b ranger tig git vim"}], "co2_eq_emissions": 7.351885824089346}
transformers pytorch bert text-classification autotrain ja dataset:vabadeh213/autotrain-data-iine_classification10 co2_eq_emissions autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #text-classification #autotrain #ja #dataset-vabadeh213/autotrain-data-iine_classification10 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 


# Model Trained Using AutoTrain

- Problem type: Binary Classification
- Model ID: 737422470
- CO2 Emissions (in grams): 7.351885824089346

## Validation Metrics

- Loss: 0.39456263184547424
- Accuracy: 0.8279088689991864
- Precision: 0.6869806094182825
- Recall: 0.17663817663817663
- AUC: 0.7937892215111646
- F1: 0.2810198300283286

## Usage

You can use cURL to access this model:



Or Python API:


# Model Trained Using AutoTrain

- Problem type: Binary Classification
- Model ID: 737422470
- CO2 Emissions (in grams): 7.351885824089346 ## Validation Metrics

- Loss: 0.39456263184547424
- Accuracy: 0.8279088689991864
- Precision: 0.6869806094182825
- Recall: 0.17663817663817663
- AUC: 0.7937892215111646
- F1: 0.2810198300283286 ## Usage

You can use cURL to access this model:



Or Python API:
TAGS
#transformers #pytorch #bert #text-classification #autotrain #ja #dataset-vabadeh213/autotrain-data-iine_classification10 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
 # Model Trained Using AutoTrain

- Problem type: Binary Classification
- Model ID: 737422470
- CO2 Emissions (in grams): 7.351885824089346 ## Validation Metrics

- Loss: 0.39456263184547424
- Accuracy: 0.8279088689991864
- Precision: 0.6869806094182825
- Recall: 0.17663817663817663
- AUC: 0.7937892215111646
- F1: 0.2810198300283286 ## Usage

You can use cURL to access this model:



Or Python API:
passage: TAGS
#transformers #pytorch #bert #text-classification #autotrain #ja #dataset-vabadeh213/autotrain-data-iine_classification10 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
# Model Trained Using AutoTrain

- Problem type: Binary Classification
- Model ID: 737422470
- CO2 Emissions (in grams): 7.351885824089346## Validation Metrics

- Loss: 0.39456263184547424
- Accuracy: 0.8279088689991864
- Precision: 0.6869806094182825
- Recall: 0.17663817663817663
- AUC: 0.7937892215111646
- F1: 0.2810198300283286## Usage

You can use cURL to access this model:



Or Python API: