Instructions to use connectivity/bert_ft_qqp-72 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use connectivity/bert_ft_qqp-72 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="connectivity/bert_ft_qqp-72")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("connectivity/bert_ft_qqp-72") model = AutoModelForSequenceClassification.from_pretrained("connectivity/bert_ft_qqp-72", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
-
Upload qqp_eval@34110steps_bert_ft_qqp-72.json with git-lfs
632ae0b -
Upload paws_eval@34110steps_bert_ft_qqp-72.json
3a7a320 -
Upload paws_eval@25000steps_bert_ft_qqp-72.json
ae04e43 -
Upload paws_eval@15000steps_bert_ft_qqp-72.json
1a22546 -
Added training logs
2664fce -
Saving weights and logs of step 34110
3bbaa64 -
Saving weights and logs of step 30000
464156c -
Saving weights and logs of step 25000
4ec2b6c -
Saving weights and logs of step 20000
bed2414 -
Saving weights and logs of step 15000
8cbae90 -
Saving tokenizer
ae80855 -
initial commit
2246ab1