
# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 52472123757
- CO2 Emissions (in grams): 4.1360

This model reuses and extends a Bert model trained on [NicholasSynovic/Free-AutoTrain-VEAA](https://huggingface.co/datasets/NicholasSynovic/Free-AutoTrain-VEAA)

## Validation Metrics

- Loss: 1.425
- Accuracy: 0.636
- Macro F1: 0.504
- Micro F1: 0.636
- Weighted F1: 0.624
- Macro Precision: 0.523
- Micro Precision: 0.636
- Weighted Precision: 0.630
- Macro Recall: 0.508
- Micro Recall: 0.636
- Weighted Recall: 0.636


## 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/NicholasSynovic/autotrain-luc-comp429-victorian-authorship-classification-52472123757
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("NicholasSynovic/AutoTrain-LUC-COMP429-VEAA-Classification", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("NicholasSynovic/autotrain-luc-comp429-victorian-authorship-classification-52472123757", use_auth_token=True)

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

outputs = model(**inputs)
```
{"language": ["en"], "license": "agpl-3.0", "tags": ["autotrain", "text-classification"], "datasets": ["NicholasSynovic/autotrain-data-luc-comp429-victorian-authorship-classification"], "metrics": ["accuracy", "f1", "recall", "bertscore"], "widget": [{"text": "I love AutoTrain"}], "co2_eq_emissions": {"emissions": 4.1359796275464005}, "pipeline_tag": "text-classification"}
transformers pytorch safetensors bert text-classification autotrain en dataset:NicholasSynovic/autotrain-data-luc-comp429-victorian-authorship-classification license:agpl-3.0 co2_eq_emissions autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #safetensors #bert #text-classification #autotrain #en #dataset-NicholasSynovic/autotrain-data-luc-comp429-victorian-authorship-classification #license-agpl-3.0 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 


# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 52472123757
- CO2 Emissions (in grams): 4.1360

This model reuses and extends a Bert model trained on NicholasSynovic/Free-AutoTrain-VEAA

## Validation Metrics

- Loss: 1.425
- Accuracy: 0.636
- Macro F1: 0.504
- Micro F1: 0.636
- Weighted F1: 0.624
- Macro Precision: 0.523
- Micro Precision: 0.636
- Weighted Precision: 0.630
- Macro Recall: 0.508
- Micro Recall: 0.636
- Weighted Recall: 0.636


## Usage

You can use cURL to access this model:



Or Python API:


# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 52472123757
- CO2 Emissions (in grams): 4.1360

This model reuses and extends a Bert model trained on NicholasSynovic/Free-AutoTrain-VEAA ## Validation Metrics

- Loss: 1.425
- Accuracy: 0.636
- Macro F1: 0.504
- Micro F1: 0.636
- Weighted F1: 0.624
- Macro Precision: 0.523
- Micro Precision: 0.636
- Weighted Precision: 0.630
- Macro Recall: 0.508
- Micro Recall: 0.636
- Weighted Recall: 0.636 ## Usage

You can use cURL to access this model:



Or Python API:
TAGS
#transformers #pytorch #safetensors #bert #text-classification #autotrain #en #dataset-NicholasSynovic/autotrain-data-luc-comp429-victorian-authorship-classification #license-agpl-3.0 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
 # Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 52472123757
- CO2 Emissions (in grams): 4.1360

This model reuses and extends a Bert model trained on NicholasSynovic/Free-AutoTrain-VEAA ## Validation Metrics

- Loss: 1.425
- Accuracy: 0.636
- Macro F1: 0.504
- Micro F1: 0.636
- Weighted F1: 0.624
- Macro Precision: 0.523
- Micro Precision: 0.636
- Weighted Precision: 0.630
- Macro Recall: 0.508
- Micro Recall: 0.636
- Weighted Recall: 0.636 ## Usage

You can use cURL to access this model:



Or Python API:
TAGS
#transformers #pytorch #safetensors #bert #text-classification #autotrain #en #dataset-NicholasSynovic/autotrain-data-luc-comp429-victorian-authorship-classification #license-agpl-3.0 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 52472123757
- CO2 Emissions (in grams): 4.1360

This model reuses and extends a Bert model trained on NicholasSynovic/Free-AutoTrain-VEAA## Validation Metrics

- Loss: 1.425
- Accuracy: 0.636
- Macro F1: 0.504
- Micro F1: 0.636
- Weighted F1: 0.624
- Macro Precision: 0.523
- Micro Precision: 0.636
- Weighted Precision: 0.630
- Macro Recall: 0.508
- Micro Recall: 0.636
- Weighted Recall: 0.636## Usage

You can use cURL to access this model:



Or Python API: