
# Model Trained Using AutoTrain

- Problem type: Entity Extraction
- Model ID: 1044135953
- CO2 Emissions (in grams): 1.425282392185522

## Validation Metrics

- Loss: 0.4587894678115845
- Accuracy: 0.8957797220792589
- Precision: 0.553921568627451
- Recall: 0.6793587174348698
- F1: 0.6102610261026103

## 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/danielmantisnlp/autotrain-oms-ner-bi-1044135953
```

Or Python API:

```
from transformers import AutoModelForTokenClassification, AutoTokenizer

model = AutoModelForTokenClassification.from_pretrained("danielmantisnlp/autotrain-oms-ner-bi-1044135953", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("danielmantisnlp/autotrain-oms-ner-bi-1044135953", use_auth_token=True)

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

outputs = model(**inputs)
```
{"language": "en", "tags": "autotrain", "datasets": ["danielmantisnlp/autotrain-data-oms-ner-bi"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 1.425282392185522}
transformers pytorch bert token-classification autotrain en dataset:danielmantisnlp/autotrain-data-oms-ner-bi co2_eq_emissions autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #token-classification #autotrain #en #dataset-danielmantisnlp/autotrain-data-oms-ner-bi #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 


# Model Trained Using AutoTrain

- Problem type: Entity Extraction
- Model ID: 1044135953
- CO2 Emissions (in grams): 1.425282392185522

## Validation Metrics

- Loss: 0.4587894678115845
- Accuracy: 0.8957797220792589
- Precision: 0.553921568627451
- Recall: 0.6793587174348698
- F1: 0.6102610261026103

## Usage

You can use cURL to access this model:



Or Python API:


# Model Trained Using AutoTrain

- Problem type: Entity Extraction
- Model ID: 1044135953
- CO2 Emissions (in grams): 1.425282392185522 ## Validation Metrics

- Loss: 0.4587894678115845
- Accuracy: 0.8957797220792589
- Precision: 0.553921568627451
- Recall: 0.6793587174348698
- F1: 0.6102610261026103 ## Usage

You can use cURL to access this model:



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

- Problem type: Entity Extraction
- Model ID: 1044135953
- CO2 Emissions (in grams): 1.425282392185522 ## Validation Metrics

- Loss: 0.4587894678115845
- Accuracy: 0.8957797220792589
- Precision: 0.553921568627451
- Recall: 0.6793587174348698
- F1: 0.6102610261026103 ## Usage

You can use cURL to access this model:



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

- Problem type: Entity Extraction
- Model ID: 1044135953
- CO2 Emissions (in grams): 1.425282392185522## Validation Metrics

- Loss: 0.4587894678115845
- Accuracy: 0.8957797220792589
- Precision: 0.553921568627451
- Recall: 0.6793587174348698
- F1: 0.6102610261026103## Usage

You can use cURL to access this model:



Or Python API: