
# Model Finetuned from BERT-base for

- Problem type: Multi-class Classification
- Model ID: 25805800

## Validation Metrics

- Loss: 0.4422711133956909
- Accuracy: 0.8615328555811976
- Macro F1: 0.8642434650461513
- Micro F1: 0.8615328555811976
- Weighted F1: 0.8617743626671308
- Macro Precision: 0.8649112225076049
- Micro Precision: 0.8615328555811976
- Weighted Precision: 0.8625407179375096
- Macro Recall: 0.8640777539828228
- Micro Recall: 0.8615328555811976
- Weighted Recall: 0.8615328555811976


## Usage

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("Crasher222/kaggle-comp-test")

tokenizer = AutoTokenizer.from_pretrained("Crasher222/kaggle-comp-test")

inputs = tokenizer("I am in love with you", return_tensors="pt")

outputs = model(**inputs)
```
{"language": "en", "tags": "autonlp", "datasets": ["Crasher222/autonlp-data-kaggle-test"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 60.744727079482495}
transformers pytorch bert text-classification autonlp en dataset:Crasher222/autonlp-data-kaggle-test co2_eq_emissions autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #text-classification #autonlp #en #dataset-Crasher222/autonlp-data-kaggle-test #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 


# Model Finetuned from BERT-base for

- Problem type: Multi-class Classification
- Model ID: 25805800

## Validation Metrics

- Loss: 0.4422711133956909
- Accuracy: 0.8615328555811976
- Macro F1: 0.8642434650461513
- Micro F1: 0.8615328555811976
- Weighted F1: 0.8617743626671308
- Macro Precision: 0.8649112225076049
- Micro Precision: 0.8615328555811976
- Weighted Precision: 0.8625407179375096
- Macro Recall: 0.8640777539828228
- Micro Recall: 0.8615328555811976
- Weighted Recall: 0.8615328555811976


## Usage


# Model Finetuned from BERT-base for

- Problem type: Multi-class Classification
- Model ID: 25805800 ## Validation Metrics

- Loss: 0.4422711133956909
- Accuracy: 0.8615328555811976
- Macro F1: 0.8642434650461513
- Micro F1: 0.8615328555811976
- Weighted F1: 0.8617743626671308
- Macro Precision: 0.8649112225076049
- Micro Precision: 0.8615328555811976
- Weighted Precision: 0.8625407179375096
- Macro Recall: 0.8640777539828228
- Micro Recall: 0.8615328555811976
- Weighted Recall: 0.8615328555811976 ## Usage
TAGS
#transformers #pytorch #bert #text-classification #autonlp #en #dataset-Crasher222/autonlp-data-kaggle-test #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
 # Model Finetuned from BERT-base for

- Problem type: Multi-class Classification
- Model ID: 25805800 ## Validation Metrics

- Loss: 0.4422711133956909
- Accuracy: 0.8615328555811976
- Macro F1: 0.8642434650461513
- Micro F1: 0.8615328555811976
- Weighted F1: 0.8617743626671308
- Macro Precision: 0.8649112225076049
- Micro Precision: 0.8615328555811976
- Weighted Precision: 0.8625407179375096
- Macro Recall: 0.8640777539828228
- Micro Recall: 0.8615328555811976
- Weighted Recall: 0.8615328555811976 ## Usage
passage: TAGS
#transformers #pytorch #bert #text-classification #autonlp #en #dataset-Crasher222/autonlp-data-kaggle-test #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
# Model Finetuned from BERT-base for

- Problem type: Multi-class Classification
- Model ID: 25805800## Validation Metrics

- Loss: 0.4422711133956909
- Accuracy: 0.8615328555811976
- Macro F1: 0.8642434650461513
- Micro F1: 0.8615328555811976
- Weighted F1: 0.8617743626671308
- Macro Precision: 0.8649112225076049
- Micro Precision: 0.8615328555811976
- Weighted Precision: 0.8625407179375096
- Macro Recall: 0.8640777539828228
- Micro Recall: 0.8615328555811976
- Weighted Recall: 0.8615328555811976## Usage