
# Model Trained Using AutoTrain

- Problem type: Entity Extraction
- Model ID: 1181244086
- CO2 Emissions (in grams): 0.004663044473485149

## Validation Metrics

- Loss: 0.5532978773117065
- Accuracy: 0.8263097949886105
- Precision: 0.5104166666666666
- Recall: 0.4681528662420382
- F1: 0.4883720930232558

## 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/Shenzy2/autotrain-tk-1181244086
```

Or Python API:

```
from transformers import AutoModelForTokenClassification, AutoTokenizer

model = AutoModelForTokenClassification.from_pretrained("Shenzy2/autotrain-tk-1181244086", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("Shenzy2/autotrain-tk-1181244086", use_auth_token=True)

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

outputs = model(**inputs)
```
{"language": "en", "tags": "autotrain", "datasets": ["Shenzy2/autotrain-data-tk"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 0.004663044473485149}
transformers pytorch bert token-classification autotrain en dataset:Shenzy2/autotrain-data-tk co2_eq_emissions autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #token-classification #autotrain #en #dataset-Shenzy2/autotrain-data-tk #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 


# Model Trained Using AutoTrain

- Problem type: Entity Extraction
- Model ID: 1181244086
- CO2 Emissions (in grams): 0.004663044473485149

## Validation Metrics

- Loss: 0.5532978773117065
- Accuracy: 0.8263097949886105
- Precision: 0.5104166666666666
- Recall: 0.4681528662420382
- F1: 0.4883720930232558

## Usage

You can use cURL to access this model:



Or Python API:


# Model Trained Using AutoTrain

- Problem type: Entity Extraction
- Model ID: 1181244086
- CO2 Emissions (in grams): 0.004663044473485149 ## Validation Metrics

- Loss: 0.5532978773117065
- Accuracy: 0.8263097949886105
- Precision: 0.5104166666666666
- Recall: 0.4681528662420382
- F1: 0.4883720930232558 ## Usage

You can use cURL to access this model:



Or Python API:
TAGS
#transformers #pytorch #bert #token-classification #autotrain #en #dataset-Shenzy2/autotrain-data-tk #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
 # Model Trained Using AutoTrain

- Problem type: Entity Extraction
- Model ID: 1181244086
- CO2 Emissions (in grams): 0.004663044473485149 ## Validation Metrics

- Loss: 0.5532978773117065
- Accuracy: 0.8263097949886105
- Precision: 0.5104166666666666
- Recall: 0.4681528662420382
- F1: 0.4883720930232558 ## Usage

You can use cURL to access this model:



Or Python API:
passage: TAGS
#transformers #pytorch #bert #token-classification #autotrain #en #dataset-Shenzy2/autotrain-data-tk #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
# Model Trained Using AutoTrain

- Problem type: Entity Extraction
- Model ID: 1181244086
- CO2 Emissions (in grams): 0.004663044473485149## Validation Metrics

- Loss: 0.5532978773117065
- Accuracy: 0.8263097949886105
- Precision: 0.5104166666666666
- Recall: 0.4681528662420382
- F1: 0.4883720930232558## Usage

You can use cURL to access this model:



Or Python API: