Instructions to use intanm/mBERT-squad with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use intanm/mBERT-squad with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "question-answering" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # pip install "transformers<5.0.0" from transformers import pipeline pipe = pipeline("question-answering", model="intanm/mBERT-squad")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("intanm/mBERT-squad") model = AutoModelForQuestionAnswering.from_pretrained("intanm/mBERT-squad", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
-
update model card README.md
c140745 -
End of training
8eee312 -
Training in progress, step 10500
6f5853e -
Training in progress, step 10000
22e1a53 -
Training in progress, step 9500
077d612 -
Training in progress, step 9000
4422bed -
Training in progress, step 8500
7accd95 -
Training in progress, step 8000
8e01f31 -
Training in progress, step 7500
fb6b715 -
Training in progress, step 7000
9de288a -
Training in progress, step 6500
6fbe739 -
Training in progress, step 6000
1205533 -
Training in progress, step 5500
06a4855 -
Training in progress, step 5000
a83d920 -
Training in progress, step 4500
1820cdd -
Training in progress, step 4000
29c0189 -
Training in progress, step 3500
e2f5411 -
Training in progress, step 3000
cae9cca -
Training in progress, step 2500
86bff65 -
Training in progress, step 2000
51ecb72 -
Training in progress, step 1500
851b785 -
Training in progress, step 1000
77dab45 -
Training in progress, step 500
74676aa -
initial commit
f554854