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autotrain-bert-base-cased-correct-test4format-50449120549
onevholy/autotrain-bert-base-cased-correct-test4format-50449120549
autotrain-bert-base-cased-correct-test4format-50449120549 is a token classification model from onevholy. Use it when you need labels on individual words, such as names. It is set up for transformers.
- Problem type: Entity Extraction - Model ID: 50449120549 - CO2 Emissions (in grams): 0.2755
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Repo size
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At a glance
- Task
- Token Classification
- Library
- transformers
- Model type
- bert
- Access
- Public
- Created
- Apr 18, 2023
- Updated
- Apr 18, 2023
- SHA
- b78aee52
Try a prompt
Model card
From the Hugging Face model README
Model Trained Using AutoTrain
- Problem type: Entity Extraction
- Model ID: 50449120549
- CO2 Emissions (in grams): 0.2755
Validation Metrics
- Loss: 0.014
- Accuracy: 1.000
- Precision: 1.000
- Recall: 1.000
- F1: 1.000
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 AutoTrain"}' https://api-inference.huggingface.co/models/onevholy/autotrain-bert-base-cased-correct-test4format-50449120549
Or Python API:
from transformers import AutoModelForTokenClassification, AutoTokenizer
model = AutoModelForTokenClassification.from_pretrained("onevholy/autotrain-bert-base-cased-correct-test4format-50449120549", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("onevholy/autotrain-bert-base-cased-correct-test4format-50449120549", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
- Task
- Token Classification
- Library
- transformers
- Type
- bert
- Languages
- unk
- Created
- Apr 18, 2023
- Updated
- Apr 18, 2023