Instructions to use research-dump/tiny_bert_temp_classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use research-dump/tiny_bert_temp_classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="research-dump/tiny_bert_temp_classifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("research-dump/tiny_bert_temp_classifier") model = AutoModelForSequenceClassification.from_pretrained("research-dump/tiny_bert_temp_classifier") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 77797a5c485af48a44aa3e334d0f3de1311cfe4a81ed74d916bcec00e3b4cfd4
- Size of remote file:
- 17.6 MB
- SHA256:
- 789cd0ff1c3bac71fedb106b2a6b7a5e03735a600eb623e2e3cb338d2805d02c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.