Instructions to use varsha12/BERT_DNRTI with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use varsha12/BERT_DNRTI with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="varsha12/BERT_DNRTI")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("varsha12/BERT_DNRTI") model = AutoModelForTokenClassification.from_pretrained("varsha12/BERT_DNRTI", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
-
Upload config.json
fdd9ff8 -
Upload pytorch_model.bin
3058fde -
Upload vocab.txt
9a4cdff -
Upload config.json
c737830
-
Delete vocabulary.json
91b970d -
Delete vocab.txt
3db44bd -
Delete config.json
7732ca9 -
Delete traced_pytorch_model.pt
a93e00c
-
Upload vocabulary.json
ed85e72 -
Upload config.json
1b74f9d -
Upload traced_pytorch_model.pt with git-lfs
65ab09b -
Delete pytorch_model.pth
9f814d9 -
Upload pytorch_model.pth with git-lfs
d06d986 -
Delete model1.pth
70c8f27 -
Upload model1.pth with git-lfs
0e5b2c5 -
Delete pytorch_model.bin
76284cc
-
Upload pytorch_model.bin with git-lfs
e0b56d1 -
Upload vocab.txt
31a1fea -
Upload config.json
207e3a0 -
initial commit
2f74452