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autonlp-tele_red_data_model-585716433
kSaluja/autonlp-tele_red_data_model-585716433
autonlp-tele_red_data_model-585716433 is a token classification model from kSaluja. Use it when you need labels on individual words, such as names. It is set up for transformers.
- Problem type: Entity Extraction - Model ID: 585716433 - CO2 Emissions (in grams): 2.379476355147211
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At a glance
- Task
- Token Classification
- Library
- transformers
- Model type
- bert
- Access
- Public
- Created
- Mar 2, 2022
- Updated
- Feb 21, 2022
- SHA
- 8cba1cd2
Try a prompt
Model card
From the Hugging Face model README
Model Trained Using AutoNLP
- Problem type: Entity Extraction
- Model ID: 585716433
- CO2 Emissions (in grams): 2.379476355147211
Validation Metrics
- Loss: 0.15210922062397003
- Accuracy: 0.9724770642201835
- Precision: 0.950836820083682
- Recall: 0.9625838333921638
- F1: 0.9566742676723382
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/kSaluja/autonlp-tele_red_data_model-585716433
Or Python API:
from transformers import AutoModelForTokenClassification, AutoTokenizer
model = AutoModelForTokenClassification.from_pretrained("kSaluja/autonlp-tele_red_data_model-585716433", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("kSaluja/autonlp-tele_red_data_model-585716433", use_auth_token=True)
inputs = tokenizer("I love AutoNLP", return_tensors="pt")
outputs = model(**inputs)
- Task
- Token Classification
- Library
- transformers
- Type
- bert
- Languages
- en
- Created
- Mar 2, 2022
- Updated
- Feb 21, 2022