Instructions to use connectivity/bert_ft_qqp-17 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use connectivity/bert_ft_qqp-17 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="connectivity/bert_ft_qqp-17")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("connectivity/bert_ft_qqp-17") model = AutoModelForSequenceClassification.from_pretrained("connectivity/bert_ft_qqp-17", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
-
Upload qqp_eval@34110steps_bert_ft_qqp-17.json with git-lfs
c2c7c72 -
Upload paws_eval@34110steps_bert_ft_qqp-17.json
39c67b7 -
Upload paws_eval@25000steps_bert_ft_qqp-17.json
2d9dd7f -
Upload paws_eval@15000steps_bert_ft_qqp-17.json
c2922b5 -
Added training logs
549906b -
Saving weights and logs of step 34110
f710923 -
Saving weights and logs of step 30000
aa7f3c5 -
Saving weights and logs of step 25000
5505b1a -
Saving weights and logs of step 20000
8ed352a -
Saving weights and logs of step 15000
4063892 -
Saving tokenizer
8dfe227 -
initial commit
4d941ef