
# Model Trained Using AutoTrain

- Problem type: Entity Extraction
- Model ID: 1100540141
- CO2 Emissions (in grams): 8.343592303925112

## Validation Metrics

- Loss: 0.38094884157180786
- Accuracy: 0.8795777325860159
- Precision: 0.8171375141922127
- Recall: 0.8417033571821684
- F1: 0.8292385373953709

## 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/ScarlettSun9/autotrain-ZuoZhuan-1100540141
```

Or Python API:

```
from transformers import AutoModelForTokenClassification, AutoTokenizer

model = AutoModelForTokenClassification.from_pretrained("ScarlettSun9/autotrain-ZuoZhuan-1100540141", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("ScarlettSun9/autotrain-ZuoZhuan-1100540141", use_auth_token=True)

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

outputs = model(**inputs)
```
{"language": "unk", "tags": "autotrain", "datasets": ["ScarlettSun9/autotrain-data-ZuoZhuan"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 8.343592303925112}
transformers pytorch roberta token-classification autotrain unk dataset:ScarlettSun9/autotrain-data-ZuoZhuan co2_eq_emissions autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #roberta #token-classification #autotrain #unk #dataset-ScarlettSun9/autotrain-data-ZuoZhuan #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 


# Model Trained Using AutoTrain

- Problem type: Entity Extraction
- Model ID: 1100540141
- CO2 Emissions (in grams): 8.343592303925112

## Validation Metrics

- Loss: 0.38094884157180786
- Accuracy: 0.8795777325860159
- Precision: 0.8171375141922127
- Recall: 0.8417033571821684
- F1: 0.8292385373953709

## Usage

You can use cURL to access this model:



Or Python API:


# Model Trained Using AutoTrain

- Problem type: Entity Extraction
- Model ID: 1100540141
- CO2 Emissions (in grams): 8.343592303925112 ## Validation Metrics

- Loss: 0.38094884157180786
- Accuracy: 0.8795777325860159
- Precision: 0.8171375141922127
- Recall: 0.8417033571821684
- F1: 0.8292385373953709 ## Usage

You can use cURL to access this model:



Or Python API:
TAGS
#transformers #pytorch #roberta #token-classification #autotrain #unk #dataset-ScarlettSun9/autotrain-data-ZuoZhuan #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
 # Model Trained Using AutoTrain

- Problem type: Entity Extraction
- Model ID: 1100540141
- CO2 Emissions (in grams): 8.343592303925112 ## Validation Metrics

- Loss: 0.38094884157180786
- Accuracy: 0.8795777325860159
- Precision: 0.8171375141922127
- Recall: 0.8417033571821684
- F1: 0.8292385373953709 ## Usage

You can use cURL to access this model:



Or Python API:
passage: TAGS
#transformers #pytorch #roberta #token-classification #autotrain #unk #dataset-ScarlettSun9/autotrain-data-ZuoZhuan #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
# Model Trained Using AutoTrain

- Problem type: Entity Extraction
- Model ID: 1100540141
- CO2 Emissions (in grams): 8.343592303925112## Validation Metrics

- Loss: 0.38094884157180786
- Accuracy: 0.8795777325860159
- Precision: 0.8171375141922127
- Recall: 0.8417033571821684
- F1: 0.8292385373953709## Usage

You can use cURL to access this model:



Or Python API: