Instructions to use harshil10/bert_small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use harshil10/bert_small with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="harshil10/bert_small")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("harshil10/bert_small") model = AutoModelForQuestionAnswering.from_pretrained("harshil10/bert_small", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 39b79494f43c2fd54c73bc7cc2ef35cea7d1b81bcdddd77348f3acabd227cbb7
- Size of remote file:
- 245 MB
- SHA256:
- 9488dac0960ab9d8f7473e258353baa619925b684c2ab726ce65774d9f1cb12b
路
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.