metadata
tags: autonlp
language: en
widget:
- text: I am still waiting on my card?
datasets:
- banking77
model-index:
- name: RoBERTa-Banking77
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: BANKING77
type: banking77
metrics:
- name: Accuracy
type: accuracy
value: 93.51
- name: Macro F1
type: macro-f1
value: 93.49
- name: Weighted F1
type: weighted-f1
value: 93.49
- task:
type: text-classification
name: Text Classification
dataset:
name: banking77
type: banking77
config: default
split: test
metrics:
- name: Accuracy
type: accuracy
value: 0.026298701298701297
verified: true
- name: Precision Macro
type: precision
value: 0.026592805946180877
verified: true
- name: Precision Micro
type: precision
value: 0.026298701298701297
verified: true
- name: Precision Weighted
type: precision
value: 0.026592805946180874
verified: true
- name: Recall Macro
type: recall
value: 0.026298701298701297
verified: true
- name: Recall Micro
type: recall
value: 0.026298701298701297
verified: true
- name: Recall Weighted
type: recall
value: 0.026298701298701297
verified: true
- name: F1 Macro
type: f1
value: 0.026443240463879983
verified: true
- name: F1 Micro
type: f1
value: 0.026298701298701297
verified: true
- name: F1 Weighted
type: f1
value: 0.026443240463879983
verified: true
- name: loss
type: loss
value: 8.701333999633789
verified: true
RoBERTa-Banking77 trained using autoNLP
- Problem type: Multi-class Classification
Validation Metrics
- Loss: 0.27382662892341614
- Accuracy: 0.935064935064935
- Macro F1: 0.934939412967268
- Micro F1: 0.935064935064935
- Weighted F1: 0.934939412967268
- Macro Precision: 0.9372295644352715
- Micro Precision: 0.935064935064935
- Weighted Precision: 0.9372295644352717
- Macro Recall: 0.9350649350649349
- Micro Recall: 0.935064935064935
- Weighted Recall: 0.935064935064935
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 AutoNLP"}' https://api-inference.huggingface.co/models/philschmid/RoBERTa-Banking77
Or Python API:
from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
model_id = 'philschmid/RoBERTa-Banking77'
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForSequenceClassification.from_pretrained(model_id)
classifier = pipeline('text-classification', tokenizer=tokenizer, model=model)
classifier('What is the base of the exchange rates?')