Instructions to use cduesing/bert_workforce with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cduesing/bert_workforce with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="cduesing/bert_workforce")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("cduesing/bert_workforce") model = AutoModelForSequenceClassification.from_pretrained("cduesing/bert_workforce", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
-
Update config.json
f3e06ef -
Update config.json
2beda03 -
Update config.json
81520fa -
Update config.json
bdd42fa -
Update config.json
02a09c2 -
Upload vocab.txt
cca4263 -
Upload tokenizer_config.json
0984492 -
Upload tokenizer.json
e219c0c -
Delete tokenizer_config.json
9766136 -
Delete tokenizer.json
73a88ab -
Delete vocab.txt
200d9a9 -
Update config.json
0c4a07c -
Update config.json
3d2d62e -
Update config.json
8549a3e -
Update tokenizer_config.json
a7745bd -
Update config.json
7f0c613 -
Update config.json
c86b3ea -
Upload tokenizer.json
e3f8212 -
Upload tokenizer_config.json
dab80ac -
Upload vocab.txt
44f8b0b -
Upload pytorch_model.bin
a8d15e7 -
Upload config.json
2349773 -
initial commit
8bd65f9