Downloads · 30 days
33
2% of all-time downloads
RoBERTa-Banking77
philschmid/RoBERTa-Banking77
RoBERTa-Banking77 is a text classification model from philschmid. Use it when you need a label for a piece of text. It is set up for transformers.
- Problem type: Multi-class Classification
All-time downloads
1.6K
Public
Repo size
657 MB
Likes
0
Public
Repo makeup
Click a slice to open those files.
.bin329 MB · 99%
At a glance
- Task
- Text Classification
- Library
- transformers
- Model type
- roberta
- Access
- Public
- Created
- Mar 2, 2022
- Updated
- Nov 4, 2021
- SHA
- e45f9df5
Try a prompt
Datasets
Model card
From the Hugging Face model README
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?')
- Task
- Text Classification
- Library
- transformers
- Type
- roberta
- Languages
- en
- Created
- Mar 2, 2022
- Updated
- Nov 4, 2021