Instructions to use connectivity/bert_ft_qqp-76 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use connectivity/bert_ft_qqp-76 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="connectivity/bert_ft_qqp-76")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("connectivity/bert_ft_qqp-76") model = AutoModelForSequenceClassification.from_pretrained("connectivity/bert_ft_qqp-76", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
-
Upload qqp_eval@34110steps_bert_ft_qqp-76.json with git-lfs
b052173 -
Upload paws_eval@34110steps_bert_ft_qqp-76.json
1dc4a3e -
Upload paws_eval@25000steps_bert_ft_qqp-76.json
3a53d31 -
Upload paws_eval@15000steps_bert_ft_qqp-76.json
574a9d3 -
Added training logs
3645136 -
Saving weights and logs of step 34110
9813ad6 -
Saving weights and logs of step 30000
cf59f87 -
Saving weights and logs of step 25000
7e29c11 -
Saving weights and logs of step 20000
3d1f960 -
Saving weights and logs of step 15000
569593d -
Saving tokenizer
fae5cfd -
initial commit
1d4224f