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--- |
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tags: autotrain |
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language: en |
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widget: |
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- text: "I love AutoTrain \U0001F917" |
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datasets: |
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- lewtun/autotrain-data-acronym-identification |
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- acronym_identification |
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co2_eq_emissions: 10.435358044493652 |
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model-index: |
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- name: autotrain-demo |
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results: |
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- task: |
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name: Text Classification |
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type: text-classification |
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dataset: |
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name: acronym_identification |
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type: acronym_identification |
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args: default |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.9708090976211485 |
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- task: |
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type: token-classification |
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name: Token Classification |
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dataset: |
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name: acronym_identification |
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type: acronym_identification |
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config: default |
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split: train |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.9790777669399117 |
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verified: true |
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- name: Precision |
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type: precision |
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value: 0.9197835301644851 |
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verified: true |
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- name: Recall |
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type: recall |
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value: 0.946479027789208 |
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verified: true |
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- name: F1 |
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type: f1 |
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value: 0.9329403493591477 |
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verified: true |
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- name: loss |
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type: loss |
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value: 0.06360606849193573 |
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verified: true |
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--- |
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# Model Trained Using AutoTrain |
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- Problem type: Entity Extraction |
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- Model ID: 7324788 |
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- CO2 Emissions (in grams): 10.435358044493652 |
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## Validation Metrics |
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|
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- Loss: 0.08991389721632004 |
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- Accuracy: 0.9708090976211485 |
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- Precision: 0.8998421675654347 |
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- Recall: 0.9309429854401959 |
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- F1: 0.9151284109149278 |
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## Usage |
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You can use cURL to access this model: |
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``` |
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$ 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 |
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``` |
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Or Python API: |
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``` |
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from transformers import AutoModelForTokenClassification, AutoTokenizer |
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model = AutoModelForTokenClassification.from_pretrained("lewtun/autotrain-acronym-identification-7324788", use_auth_token=True) |
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tokenizer = AutoTokenizer.from_pretrained("lewtun/autotrain-acronym-identification-7324788", use_auth_token=True) |
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inputs = tokenizer("I love AutoTrain", return_tensors="pt") |
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outputs = model(**inputs) |
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``` |