Instructions to use connectivity/bert_ft_qqp-7 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use connectivity/bert_ft_qqp-7 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="connectivity/bert_ft_qqp-7")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("connectivity/bert_ft_qqp-7") model = AutoModelForSequenceClassification.from_pretrained("connectivity/bert_ft_qqp-7", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
-
Upload qqp_eval@34110steps_bert_ft_qqp-7.json with git-lfs
604fdcd -
Upload paws_eval@34110steps_bert_ft_qqp-7.json
0c5558a -
Upload paws_eval@25000steps_bert_ft_qqp-7.json
c91837e -
Upload paws_eval@15000steps_bert_ft_qqp-7.json
c3ad799 -
Added training logs
8d67bbd -
Saving weights and logs of step 34110
f76ff61 -
Saving weights and logs of step 30000
65229c8 -
Saving weights and logs of step 25000
519a1b6 -
Saving weights and logs of step 20000
5c46a95 -
Saving weights and logs of step 15000
46fd9e4 -
Saving tokenizer
38596a2 -
initial commit
55573e1