Instructions to use connectivity/bert_ft_qqp-64 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use connectivity/bert_ft_qqp-64 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="connectivity/bert_ft_qqp-64")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("connectivity/bert_ft_qqp-64") model = AutoModelForSequenceClassification.from_pretrained("connectivity/bert_ft_qqp-64", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
-
Upload qqp_eval@34110steps_bert_ft_qqp-64.json with git-lfs
b8b68c6 -
Upload paws_eval@34110steps_bert_ft_qqp-64.json
5c3f0ef -
Upload paws_eval@25000steps_bert_ft_qqp-64.json
4380947 -
Upload paws_eval@15000steps_bert_ft_qqp-64.json
d086fef -
Added training logs
04cd2da -
Saving weights and logs of step 34110
d4589d1 -
Saving weights and logs of step 30000
40d44a3 -
Saving weights and logs of step 25000
3136c0e -
Saving weights and logs of step 20000
d157c79 -
Saving weights and logs of step 15000
bd6feb7 -
Saving tokenizer
ff2478e -
initial commit
44339e2