Downloads · 30 days
4
1% of all-time downloads
autotrain-historic-sv-51079121359
peanutacake/autotrain-historic-sv-51079121359
autotrain-historic-sv-51079121359 is a token classification model from peanutacake. Use it when you need labels on individual words, such as names. It is set up for transformers.
- Problem type: Entity Extraction - Model ID: 51079121359 - CO2 Emissions (in grams): 0.4491
All-time downloads
388
Public
Repo size
994 MB
Likes
0
Public
Repo makeup
Click a slice to open those files.
.bin497 MB · 100%
At a glance
- Task
- Token Classification
- Library
- transformers
- Model type
- bert
- Access
- Public
- Created
- Apr 20, 2023
- Updated
- Apr 20, 2023
- SHA
- 5668794b
Try a prompt
Model card
From the Hugging Face model README
Model Trained Using AutoTrain
- Problem type: Entity Extraction
- Model ID: 51079121359
- CO2 Emissions (in grams): 0.4491
Validation Metrics
- Loss: 0.155
- Accuracy: 0.961
- Precision: 0.000
- Recall: 0.000
- F1: 0.000
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/peanutacake/autotrain-historic-sv-51079121359
Or Python API:
from transformers import AutoModelForTokenClassification, AutoTokenizer
model = AutoModelForTokenClassification.from_pretrained("peanutacake/autotrain-historic-sv-51079121359", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("peanutacake/autotrain-historic-sv-51079121359", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
- Task
- Token Classification
- Library
- transformers
- Type
- bert
- Languages
- sv
- Created
- Apr 20, 2023
- Updated
- Apr 20, 2023