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autotrain-17102022_only_sceond_label_no_split-1783361880
teacookies/autotrain-17102022_only_sceond_label_no_split-1783361880
autotrain-17102022_only_sceond_label_no_split-1783361880 is a token classification model from teacookies. Use it when you need labels on individual words, such as names. It is set up for transformers.
- Problem type: Entity Extraction - Model ID: 1783361880 - CO2 Emissions (in grams): 18.1795
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At a glance
- Task
- Token Classification
- Library
- transformers
- Access
- Public
- Created
- Oct 17, 2022
- Updated
- Oct 17, 2022
- SHA
- 5c5288fa
Try a prompt
Model card
From the Hugging Face model README
Model Trained Using AutoTrain
- Problem type: Entity Extraction
- Model ID: 1783361880
- CO2 Emissions (in grams): 18.1795
Validation Metrics
- Loss: 0.013
- Accuracy: 0.997
- Precision: 0.834
- Recall: 0.875
- F1: 0.854
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/teacookies/autotrain-17102022_only_sceond_label_no_split-1783361880
Or Python API:
from transformers import AutoModelForTokenClassification, AutoTokenizer
model = AutoModelForTokenClassification.from_pretrained("teacookies/autotrain-17102022_only_sceond_label_no_split-1783361880", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("teacookies/autotrain-17102022_only_sceond_label_no_split-1783361880", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
- Task
- Token Classification
- Library
- transformers
- Languages
- unk
- Created
- Oct 17, 2022
- Updated
- Oct 17, 2022