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--- |
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tags: autonlp |
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language: en |
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widget: |
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- text: "I am still waiting on my card?" |
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datasets: |
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- banking77 |
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model-index: |
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- name: RoBERTa-Banking77 |
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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: "BANKING77" |
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type: banking77 |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 93.51 |
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- name: Macro F1 |
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type: macro-f1 |
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value: 93.49 |
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- name: Weighted F1 |
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type: weighted-f1 |
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value: 93.49 |
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--- |
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# `RoBERTa-Banking77` trained using autoNLP |
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- Problem type: Multi-class Classification |
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## Validation Metrics |
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- Loss: 0.27382662892341614 |
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- Accuracy: 0.935064935064935 |
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- Macro F1: 0.934939412967268 |
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- Micro F1: 0.935064935064935 |
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- Weighted F1: 0.934939412967268 |
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- Macro Precision: 0.9372295644352715 |
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- Micro Precision: 0.935064935064935 |
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- Weighted Precision: 0.9372295644352717 |
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- Macro Recall: 0.9350649350649349 |
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- Micro Recall: 0.935064935064935 |
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- Weighted Recall: 0.935064935064935 |
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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 AutoNLP"}' https://api-inference.huggingface.co/models/philschmid/RoBERTa-Banking77 |
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``` |
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Or Python API: |
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```py |
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from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline |
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model_id = 'philschmid/RoBERTa-Banking77' |
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tokenizer = AutoTokenizer.from_pretrained(model_id) |
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model = AutoModelForSequenceClassification.from_pretrained(model_id) |
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classifier = pipeline('text-classification', tokenizer=tokenizer, model=model) |
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classifier('What is the base of the exchange rates?') |
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``` |
