Instructions to use connectivity/bert_ft_qqp-63 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use connectivity/bert_ft_qqp-63 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="connectivity/bert_ft_qqp-63")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("connectivity/bert_ft_qqp-63") model = AutoModelForSequenceClassification.from_pretrained("connectivity/bert_ft_qqp-63", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
-
Upload qqp_eval@34110steps_bert_ft_qqp-63.json with git-lfs
1362105 -
Upload paws_eval@34110steps_bert_ft_qqp-63.json
632e3d5 -
Upload paws_eval@25000steps_bert_ft_qqp-63.json
cc8db31 -
Upload paws_eval@15000steps_bert_ft_qqp-63.json
3b937b5 -
Added training logs
dc8e43b -
Saving weights and logs of step 34110
a6171de -
Saving weights and logs of step 30000
7d3a017 -
Saving weights and logs of step 25000
9e3f686 -
Saving weights and logs of step 20000
97e8097 -
Saving weights and logs of step 15000
ebd1601 -
Saving tokenizer
60ea014 -
initial commit
2b8516e