Instructions to use CatBarks/GPT2ES_ClassWeighted1_1bce_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CatBarks/GPT2ES_ClassWeighted1_1bce_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="CatBarks/GPT2ES_ClassWeighted1_1bce_model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("CatBarks/GPT2ES_ClassWeighted1_1bce_model") model = AutoModelForSequenceClassification.from_pretrained("CatBarks/GPT2ES_ClassWeighted1_1bce_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ac5f4970f35e1945373be73e7ad748f6912b4837ba3bcb31cbaa838acafc26e9
- Size of remote file:
- 498 MB
- SHA256:
- 685d2dcd4a113d84c2dad377fd6a1482a5c588ce7a181f711769f026475b3a6e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.