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autotrain-oms-ner-bi-1044135953
danielmantisnlp/autotrain-oms-ner-bi-1044135953
autotrain-oms-ner-bi-1044135953 is a token classification model from danielmantisnlp. Use it when you need labels on individual words, such as names. It is set up for transformers.
- Problem type: Entity Extraction - Model ID: 1044135953 - CO2 Emissions (in grams): 1.425282392185522
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Repo size
871 MB
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At a glance
- Task
- Token Classification
- Library
- transformers
- Model type
- bert
- Access
- Public
- Created
- Jun 27, 2022
- Updated
- Jun 27, 2022
- SHA
- cb49c258
Try a prompt
Model card
From the Hugging Face model README
Model Trained Using AutoTrain
- Problem type: Entity Extraction
- Model ID: 1044135953
- CO2 Emissions (in grams): 1.425282392185522
Validation Metrics
- Loss: 0.4587894678115845
- Accuracy: 0.8957797220792589
- Precision: 0.553921568627451
- Recall: 0.6793587174348698
- F1: 0.6102610261026103
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/danielmantisnlp/autotrain-oms-ner-bi-1044135953
Or Python API:
from transformers import AutoModelForTokenClassification, AutoTokenizer
model = AutoModelForTokenClassification.from_pretrained("danielmantisnlp/autotrain-oms-ner-bi-1044135953", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("danielmantisnlp/autotrain-oms-ner-bi-1044135953", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
- Task
- Token Classification
- Library
- transformers
- Type
- bert
- Languages
- en
- Created
- Jun 27, 2022
- Updated
- Jun 27, 2022