
# Model Trained Using AutoTrain

- Problem type: Entity Extraction
- Model ID: 7324788
- CO2 Emissions (in grams): 10.435358044493652

## Validation Metrics

- Loss: 0.08991389721632004
- Accuracy: 0.9708090976211485
- Precision: 0.8998421675654347
- Recall: 0.9309429854401959
- F1: 0.9151284109149278

## 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/lewtun/autotrain-acronym-identification-7324788
```

Or Python API:

```
from transformers import AutoModelForTokenClassification, AutoTokenizer

model = AutoModelForTokenClassification.from_pretrained("lewtun/autotrain-acronym-identification-7324788", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("lewtun/autotrain-acronym-identification-7324788", use_auth_token=True)

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

outputs = model(**inputs)
```
{"language": "en", "tags": ["autotrain"], "datasets": ["lewtun/autotrain-data-acronym-identification", "acronym_identification"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 10.435358044493652, "model-index": [{"name": "autotrain-demo", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "acronym_identification", "type": "acronym_identification", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.9708090976211485, "name": "Accuracy"}]}, {"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "acronym_identification", "type": "acronym_identification", "config": "default", "split": "train"}, "metrics": [{"type": "accuracy", "value": 0.9790777669399117, "name": "Accuracy", "verified": true}, {"type": "precision", "value": 0.9197835301644851, "name": "Precision", "verified": true}, {"type": "recall", "value": 0.946479027789208, "name": "Recall", "verified": true}, {"type": "f1", "value": 0.9329403493591477, "name": "F1", "verified": true}, {"type": "loss", "value": 0.06360606849193573, "name": "loss", "verified": true}]}, {"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "acronym_identification", "type": "acronym_identification", "config": "default", "split": "validation"}, "metrics": [{"type": "accuracy", "value": 0.9758354452761242, "name": "Accuracy", "verified": true}, {"type": "precision", "value": 0.9339674814732883, "name": "Precision", "verified": true}, {"type": "recall", "value": 0.9159344831326608, "name": "Recall", "verified": true}, {"type": "f1", "value": 0.9248630887185104, "name": "F1", "verified": true}, {"type": "loss", "value": 0.07593930512666702, "name": "loss", "verified": true}]}]}]}
transformers pytorch bert token-classification autotrain en dataset:lewtun/autotrain-data-acronym-identification dataset:acronym_identification model-index co2_eq_emissions autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #bert #token-classification #autotrain #en #dataset-lewtun/autotrain-data-acronym-identification #dataset-acronym_identification #model-index #co2_eq_emissions #autotrain_compatible #endpoints_compatible #has_space #region-us 


# Model Trained Using AutoTrain

- Problem type: Entity Extraction
- Model ID: 7324788
- CO2 Emissions (in grams): 10.435358044493652

## Validation Metrics

- Loss: 0.08991389721632004
- Accuracy: 0.9708090976211485
- Precision: 0.8998421675654347
- Recall: 0.9309429854401959
- F1: 0.9151284109149278

## Usage

You can use cURL to access this model:



Or Python API:


# Model Trained Using AutoTrain

- Problem type: Entity Extraction
- Model ID: 7324788
- CO2 Emissions (in grams): 10.435358044493652 ## Validation Metrics

- Loss: 0.08991389721632004
- Accuracy: 0.9708090976211485
- Precision: 0.8998421675654347
- Recall: 0.9309429854401959
- F1: 0.9151284109149278 ## Usage

You can use cURL to access this model:



Or Python API:
TAGS
#transformers #pytorch #bert #token-classification #autotrain #en #dataset-lewtun/autotrain-data-acronym-identification #dataset-acronym_identification #model-index #co2_eq_emissions #autotrain_compatible #endpoints_compatible #has_space #region-us 
 # Model Trained Using AutoTrain

- Problem type: Entity Extraction
- Model ID: 7324788
- CO2 Emissions (in grams): 10.435358044493652 ## Validation Metrics

- Loss: 0.08991389721632004
- Accuracy: 0.9708090976211485
- Precision: 0.8998421675654347
- Recall: 0.9309429854401959
- F1: 0.9151284109149278 ## Usage

You can use cURL to access this model:



Or Python API:
passage: TAGS
#transformers #pytorch #bert #token-classification #autotrain #en #dataset-lewtun/autotrain-data-acronym-identification #dataset-acronym_identification #model-index #co2_eq_emissions #autotrain_compatible #endpoints_compatible #has_space #region-us 
# Model Trained Using AutoTrain

- Problem type: Entity Extraction
- Model ID: 7324788
- CO2 Emissions (in grams): 10.435358044493652## Validation Metrics

- Loss: 0.08991389721632004
- Accuracy: 0.9708090976211485
- Precision: 0.8998421675654347
- Recall: 0.9309429854401959
- F1: 0.9151284109149278## Usage

You can use cURL to access this model:



Or Python API: