Instructions to use foundkim/topic_classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use foundkim/topic_classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="foundkim/topic_classifier")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("foundkim/topic_classifier") model = AutoModelForSequenceClassification.from_pretrained("foundkim/topic_classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
-
Update config.json
1af161c -
Update config.json
72de58a -
Update config.json
e261228 -
Upload pytorch_model.bin with git-lfs
Upload pytorch_model.bin052cb7a
-
Upload vocab.txt
FONDIO KimbaUpload vocab.txt6a70606 -
Upload tokenizer_config.json
FONDIO KimbaUpload tokenizer_config.jsone469aa2 -
Upload tokenizer.json
FONDIO KimbaUpload tokenizer.json41301f2 -
Upload special_tokens_map.json
FONDIO KimbaUpload special_tokens_map.json01bdabf -
Upload pytorch_model.bin with git-lfs
FONDIO KimbaUpload pytorch_model.bin82bddef -
Upload config.json
FONDIO KimbaUpload config.jsonb79e2f5 -
initial commit
f891a2f