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---
tags: autotrain
language: en
widget:
- text: "I love AutoTrain \U0001F917"
datasets:
- lewtun/autotrain-data-acronym-identification
- acronym_identification
co2_eq_emissions: 10.435358044493652
model-index:
- name: autotrain-demo
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: acronym_identification
type: acronym_identification
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9708090976211485
- task:
type: token-classification
name: Token Classification
dataset:
name: acronym_identification
type: acronym_identification
config: default
split: train
metrics:
- name: Accuracy
type: accuracy
value: 0.9790777669399117
verified: true
- name: Precision
type: precision
value: 0.9197835301644851
verified: true
- name: Recall
type: recall
value: 0.946479027789208
verified: true
- name: F1
type: f1
value: 0.9329403493591477
verified: true
- name: loss
type: loss
value: 0.06360606849193573
verified: true
---
# 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)
``` |