Instructions to use iproskurina/tda-bert-en-cola with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use iproskurina/tda-bert-en-cola with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="iproskurina/tda-bert-en-cola")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("iproskurina/tda-bert-en-cola") model = AutoModelForSequenceClassification.from_pretrained("iproskurina/tda-bert-en-cola", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 319516650078bec14dc1cc3663765b1f8bf9480977ede0d50235e170df624cef
- Size of remote file:
- 3.5 kB
- SHA256:
- fce56c743ce594f7170ca8cb6b5f2c824ad4647037a1802494707195d66e1189
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.