Instructions to use 96harsh56/bert_test1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use 96harsh56/bert_test1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="96harsh56/bert_test1")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("96harsh56/bert_test1") model = AutoModelForQuestionAnswering.from_pretrained("96harsh56/bert_test1") - Notebooks
- Google Colab
- Kaggle
Add evaluation results on the default config and train split of social_i_qa
#2
by autoevaluator HF Staff - opened
Beep boop, I am a bot from Hugging Face's automatic model evaluator 👋!
Your model has been evaluated on the default config and train split of the social_i_qa dataset by @kingmbc , using the predictions stored here.
Accept this pull request to see the results displayed on the Hub leaderboard.
Evaluate your model on more datasets here.