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alberti-stanzas
alvp/alberti-stanzas
alberti-stanzas is a text classification model from alvp. Use it when you need a label for a piece of text. It is set up for transformers.
- Problem type: Multi-class Classification - Model ID: 34318169 - CO2 Emissions (in grams): 8.612473981829835
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At a glance
- Task
- Text Classification
- Library
- transformers
- Model type
- bert
- Access
- Public
- Created
- Mar 2, 2022
- Updated
- Nov 19, 2021
- SHA
- f3173e45
Try a prompt
Model card
From the Hugging Face model README
Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 34318169
- CO2 Emissions (in grams): 8.612473981829835
Validation Metrics
- Loss: 1.3520570993423462
- Accuracy: 0.6083916083916084
- Macro F1: 0.5420169617715481
- Micro F1: 0.6083916083916084
- Weighted F1: 0.5963328136975058
- Macro Precision: 0.5864033493660455
- Micro Precision: 0.6083916083916084
- Weighted Precision: 0.6364793882921277
- Macro Recall: 0.5545405576555766
- Micro Recall: 0.6083916083916084
- Weighted Recall: 0.6083916083916084
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/alvp/autonlp-alberti-stanza-names-34318169
Or Python API:
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("alvp/autonlp-alberti-stanza-names-34318169", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("alvp/autonlp-alberti-stanza-names-34318169", use_auth_token=True)
inputs = tokenizer("I love AutoNLP", return_tensors="pt")
outputs = model(**inputs)
- Task
- Text Classification
- Library
- transformers
- Type
- bert
- Languages
- unk
- Created
- Mar 2, 2022
- Updated
- Nov 19, 2021